Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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    An economic approach to non-animal toxicity testing for skin sensitisation
    Leontaridou, Maria - \ 2017
    Wageningen University. Promotor(en): E.C. van Ierland, co-promotor(en): S.G.M. Gabbert; R. Landsiedel. - Wageningen : Wageningen University - ISBN 9789463431361 - 151
    animal testing alternatives - toxicity - testing - sensitivity - sensitivity analysis - bayesian theory - alternatieven voor dierproeven - toxiciteit - testen - gevoeligheid - gevoeligheidsanalyse - bayesiaanse theorie

    Chemicals applied in products, such as food products, pharmaceuticals or cosmetics, create great benefits in society while posing risks to human health and the quality of the environment. To control those risks, it is mandatory to perform risk assessments of chemicals which require information on their hazardous properties. To meet these information requirements without sacrificing large numbers of animal tests, many non-animal testing methods and strategies have become available. Given the increasing needs for assessing chemicals’ risks, toxicity testing has become costly in terms of testing costs, time and animal welfare.

    Focusing on skin sensitisation as a case study, this thesis aims at introducing an economic approach towards the optimisation of toxicity testing strategies. Chapter 2 surveys the current status of non-animal toxicity testing strategies assessing skin sensitisation and compares criteria suggested in the toxicological literature with the conceptual and informational criteria introduced in this chapter for increasing resource-efficiency in the development of testing strategies. Chapter 3 extends to the development of a Bayesian Value-of-Information model for the optimisation of non-animal toxicity testing strategies. This optimisation model is applied to construct optimal non-animal toxicity testing strategies for the assessment of skin sensitisation potential.

    Chapter 4 focuses on the precision of testing methods and the impact of limited precision on the evaluation of test results. The borderline range of testing methods is quantified and applied as an additional evaluation measure in the prediction models of testing methods to identify substances as positive and negative (for substances yielding clear-cut test results), or as discordant (for substances yielding test results within the borderline range). Chapter 5 addresses the uncertainties underlying the predictive accuracy metrics for non-animal testing methods due to their limited precision, the sample size and composition of the samples of chemicals used to estimate the predictive capacity of testing methods. Chapters 4 and 5 focus on non-animal testing methods for the assessment of skin sensitisation potential.

    This thesis concludes that introducing the economic perspective into the construction of toxicity testing strategies is necessary to develop the means by which resource-efficiency in toxicity testing is achieved. Furthermore, the evaluation of testing methods should consider both predictivity and precision limitations such that decision makers can draw robust conclusions on the hazardous properties of chemicals.

    Expert knowledge in geostatistical inference and prediction
    Truong, N.P. - \ 2014
    Wageningen University. Promotor(en): Peter de Ruiter, co-promotor(en): Gerard Heuvelink. - Wageningen : Wageningen University - ISBN 9789462570283 - 156
    geostatistiek - biometrie - ruimtelijke statistiek - statistische inferentie - voorspelling - bayesiaanse theorie - deskundigen - kriging - geostatistics - biometry - spatial statistics - statistical inference - prediction - bayesian theory - experts - kriging

    Geostatistics provides an efficient tool for mapping environmental variables from observations and layers of explanatory variables. The number and configuration of the observations importantly determine the accuracy of geostatistical inference and prediction. Data collection is costly, and coarse sampling may lead to large uncertainties in interpolated maps. In such case, additional information may be gathered from experts who are knowledgeable about the spatial variability of environmental variables. Statistical expert elicitation has gradually become a mature research field and has proved to be able to extract from experts reliable information to form a sound scientific database. In this thesis, expert knowledge has been elicited and incorporated in geostatistical models for inference and prediction. Various extensions to the expert elicitation literature were required to make it suitable for elicitation of spatial data. The use of expert knowledge in geostatistical research is promising, yet challenging.

    Mechanistic modelling of the vertical soil organic matter profile
    Braakhekke, M.C. - \ 2014
    Wageningen University. Promotor(en): Pavel Kabat, co-promotor(en): C. Beer; M. Reichstein; Marcel Hoosbeek. - Wageningen : Wageningen University - ISBN 9789461738288 - 190
    organisch bodemmateriaal - bodemprofielen - modelleren - modellen - bayesiaanse theorie - soil organic matter - soil profiles - modeling - models - bayesian theory

    Soil organic matter (SOM) constitutes a large global pool of carbon that may play a considerable role for future climate. The vertical distribution of SOM in the profile may be important due to depth-dependence of physical, chemical, and biological conditions, and links to physical processes such as heat and moisture transport. The aim of this thesis is to develop a dynamic and mechanistic representation of the vertical SOM profile that can be applied for large scale simulations as a part of global ecosystem and earth system models.

    A model structure called SOMPROF was developed that dynamically simulates the SOM profile based on above and below ground litter input, decomposition, bioturbation, and liquid phase transport. Furthermore, three organic surface horizons are explicitly represented.

    Since the organic matter transport processes have been poorly quantified in the past and are difficult to observe directly, the model was calibrated with a Bayesian approach for two contrasting temperate forest sites in Europe. Different types of data were included in the parameter estimation, including: organic carbon stocks and concentrations, respiration rates, and excess lead-210 activity.

    The calibrations yielded good fits to the observations, and showed that the two sites differ considerably with respect to the relevance of the different processes. These differences agree well with expectations based on local conditions. However, the results also demonstrate the difficulties arising from convolution of the processes. Several parameters are poorly constrained and for one of the sites, several distinct regions in parameter space exist that yield acceptable fit.

    In a subsequent study it was found that radiocarbon observations can offer much additional constraint on several parameters, most importantly on the turnover rate of the slowest SOM fraction. Additionally, for one site, a prognostic simulation until 2100 was performed using the resulting a posterioriparameter distribution, This showed that different parts of the SOM profile can respond differently to increasing temperatures and litter input.

    In conclusion, the SOMPROF model, combined with the Bayesian calibration scheme, offers valuable insights into the relevance of the different mechanisms to the SOM profile. However, equifinality remains a challenge, particularly for distinguishing different SOM transport processes. Improved representation of liquid phase transport and incorporation of additional observations may reduce these problems. In the future, SOMPROF can be incorporated into a terrestrial ecosystem model and calibration results can be used when deriving parameter sets for large scale application.

    Probabilistic methods for robotics in agriculture
    Hiremath, S. - \ 2013
    Wageningen University. Promotor(en): A. Stein; Cajo ter Braak, co-promotor(en): Gerie van der Heijden. - S.l. : s.n. - ISBN 9789461736413 - 109
    automatisering - robots - landbouw - beeldanalyse - bayesiaanse theorie - navigatie - modelleren - automation - robots - agriculture - image analysis - bayesian theory - navigation - modeling

    Autonomous operation of robotic systems in an agricultural environment is a difficult task due to the inherent uncertainty in the environment. The robot is in a dynamic, non-deterministic and semi-structured environment with many sources of noise and a high degree of uncertainty. A novel approach dealing with uncertainty is by means of probabilistic methods. This PhD thesis studies the efficacy of probabilistic methods for autonomous robot applications in agriculture focusing on two agricultural tasks namely automatic detection of weed in a grassland and autonomous navigation of a robot in a Maize field. In automatic weed detection we look at the detection of a common weed called Rumex obtusifolius (Rumex). The suitability of image analysis for the task is examined, various existing methods are scrutinized and new probabilistic methods are proposed for robust detection of Rumex using a monocular camera in real-time. For autonomous navigation in a Maize field, probabilistic methods are developed for row following using a camera as well as a laser scanner. New sensor models are proposed to characterize the noisy measurements which are used in the navigation method for tracking the position of the robot and the plant rows. Through extensive field experiments we show that the proposed probabilistic methods are robust to varying operating conditions and conclude that probabilistic methods are essential for autonomous operation of robotic systems in an agricultural environment.

    Towards global experimental design using Bayesian networks : case studies on modeling sensory satiation
    Phan, V.A. - \ 2013
    Wageningen University. Promotor(en): Tiny van Boekel; U. Garczarek, co-promotor(en): Matthijs Dekker. - [S.l.] : s.n. - ISBN 9789461735379 - 156
    sensorische evaluatie - verzadigdheid - bayesiaanse theorie - proefopzet - wiskundige modellen - modelleren - sensory evaluation - satiety - bayesian theory - experimental design - mathematical models - modeling

    Food science problems are complex. Scientists may be able to capture more of the complexity of an investigated theme if they were able to integrate related studies. Unfortunately, individual studies are usually not designed to allow such integration, and the common statistical methods cannot be used for analyzing integrated data. The modeling technique of Bayesian networks has gained popularity in many fields of application due to its ability to deal with complexity, but has emerged only recently in food science. This thesis used data from experiments on sensory satiation as case studies. The objective was to explore the use of Bayesian networks to combine raw data of independently performed but related experiments to build a quantitative model of sensory satiation.
    Methods
    This thesis started with introducing the theoretical background of Bayesian networks to food science. The available data from various independent experiments on sensory satiation were then examined for their potential to be combined. Finally, the outcomes obtained using Bayesian networks on a single dataset were compared with the published outcomes of the respective study, in which classical statistical procedures were used to analyze the data.
    Results
    Two hurdles were identified when combining the data of related studies that were performed independently and without the intention of combining their data. The first hurdle was a lack of essential information for reliable estimations of parameters of the combined model network. This information could be obtained by deriving it from existing information in the individual studies or by performing extra experiments; these practices are, however, not always possible. The second hurdle was a possible conflict in causal relationships underlying the individual experimental designs, which can cause misleading analyses of the combined dataset. This was the case for some experiments that involved the control of secondary explanatory variables. As such, an approach termed as Global Experimental Design was proposed in this thesis as a solution to overcome these hurdles. This approach emphasizes the building of an overall network prior to designing individual studies.
    In comparison to using the classical statistical procedures, more information can be extracted using Bayesian networks. This technique could make use of the domain knowledge in a transparent manner as well as empirical data with missing values.
    Conclusions
    It is possible to combine raw data from related studies for a meaningful analysis if effort is made in the phase of experimental design. The approach of Global Experimental Design outlines this phase with the building of an overall network. By using Bayesian networks as a tool for exploratory analysis, scientists are able to gain more insights into a research domain.

    Models to relate species to environment: a hierarchical statistical approac
    Jamil, T. - \ 2012
    Wageningen University. Promotor(en): Cajo ter Braak. - S.l. : s.n. - ISBN 9789461731395 - 146
    statistiek - lineaire modellen - interacties - kenmerken - bayesiaanse theorie - plantenecologie - biostatistiek - statistics - linear models - interactions - traits - bayesian theory - plant ecology - biostatistics

    In the last two decades, the interest of community ecologists in trait-based approaches has grown dramatically and these approaches have been increasingly applied to explain and predict response of species to environmental conditions. A variety of modelling techniques are available. The dominant technique is tocluster the species based on their functional traits and then summarize the response of the clusters to environmental change. In general, fitting explicit models to data is always more informative and powerful than more informal approaches. The central theme of the thesis is how to quantify the relation of traits with the environment using three data tables, data on species occurrence and abundance in sites, data on traits of species and data on the environmental characteristics of sites. In this thesis, we place the challenge of quantifying trait-environment relationships in the context of species distribution modelling, so in the context of species-environment relationships. We present a hierarchal statistical approach to species distribution modelling that efficiently utilize the trait information and that is able to automatically select the relevant traits and environmental characteristics. This model-based approach, coupled with recent statistical developments and increased computing power, opens up possibilities that were unimaginable before.

    In the present study a hierarchical statistical approach is introduced for modeling and explaining species response along environmental gradients by species traits. The model is an extension of the generalized linear model with random terms that express the between-species variation in response to the environment. This so-called generalized linear mixed model (GLMM)is derived byintegrating a two-step procedure into one. As the basic GLMM we take the random intercept and random slope model. To introduce traits, the regression parameters (intercept and slope) are made linearly dependent on the species traits. As a consequence the trait-environment relationship is represented as an interaction term in the model. The method is illustrated using the famous Dune Meadow Data using Ellenberg indicator values as species traits.

    Niche theory proclaims that species response to environmental gradients is nonlinear. Each species has preferred an environmental condition in which it can survive and reproduce optimally. Thus each species tends to be most abundant around a specific environmental optimum and the distribution of species along any environmental gradient is usually unimodal, with the maximum at some ecological optimum.For presence-absence data, the simplest unimodal (non-negative) species response curve is the Gaussian logistic response curve with three parameters that characterize the niche: optimum (niche centre), tolerance (niche width) and maximum (expected occurrence at the centre). Niches of species differ between species and species are assumed to be evolutionary adapted. It is difficult to fit the Gaussian logistic model with linear trait submodels for the parameters with the available (generalized) nonlinear mixed model software.

    We develop the trait-modulated Gaussian logistic model in which the niche parameters are made linearly dependent on species traits. The model is fitted to data in the Bayesian frameworkusing OpenBUGS (Bayesian inference Using Gibbs Sampling).A Bayesian variable selection method is used to identify which species traits and environmental variables best explain the species data through this model. We extended the approach to find the best linear combination of environmental variables.

    We explained why and when (generalized) linear mixed models can effectively analyse unimodal data and presented a graphical tool and statistical test to test for unimodality while fitting just a generalized linear mixed model without any squared or other polynomial term. A GLMM is, of course, a linear model. Despite this fact, it can be used to detect unimodality and to fit unimodal data, with the provision that the differences in niche widthsamongspecies are not too large. As graphical tool we suggested to plot the random site effects against the environmental variable. There is an indication for unimodality, when this graph shows a quadratic relationship. The efficacy of GLMM to analyse unimodal data is illustrated by comparing the GLMM approach with an explicit unimodal model approach on simulated data and real data that show unimodality.

    When a system is described by a statistical model, model complexity leads to a very large computing time and poor estimation, especially if the number of predictors is large relative to the data size. As an alternative to and improvement over stepwise methods, shrinkage methods have been proposed. One of these is the Relevance vector machine (RVM). RVM assigns individual precisions to weights of predictors which are then estimated by maximizing the marginal likelihood (Type-II ML or empirical Bayes). We also investigated the selection properties of RVM both analytically and by experiments. We found that RVM is rather tolerant for predictors to stay in the model and concluded that RVM is not a real solution in high-dimensional data problems.

    By further study the multi-trait and multi-environmental variablemodel selection method developed that used our previous study in a linear mixed model context. The method is called tiered forward selection. In the first tier, the random factors are selected, in the second, the fixed effects are selected and in the final tier non-significant terms are removed based on a modified Akaike information criterion. The linear mixed model with the tiered forward selection is compared with Type-II ML and existing methods for detecting trait-environment relationships that are not based on mixed models, namely the fourth corner method and the linear trait-environment method (LTE).

    Bayesian Markov random field analysis for integrated network-based protein function prediction
    Kourmpetis, Y.I.A. - \ 2011
    Wageningen University. Promotor(en): Cajo ter Braak, co-promotor(en): Roeland van Ham. - [S.l.] : S.n. - ISBN 9789085859598 - 113
    statistiek - bayesiaanse theorie - markov-processen - netwerkanalyse - biostatistiek - toegepaste statistiek - bio-informatica - eiwitten - genen - moleculaire biologie - statistics - bayesian theory - markov processes - network analysis - biostatistics - applied statistics - bioinformatics - proteins - genes - molecular biology

    Unravelling the functions of proteins is one of the most important aims of modern biology. Experimental inference of protein function is expensive and not scalable to large datasets. In this thesis a probabilistic method for protein function prediction is presented that integrates different types of data such as sequences and networks. The method is based on Bayesian Markov Random Field (BMRF) analysis. BMRF was initially applied to genome wide protein function prediction using network data in yeast and in also in Arabidopsis by integrating protein domains (i.e InterPro signatures), expressions and protein protein interactions. Several of the predictions were confirmed by experimental evidence. Further, an evolutionary discrete optimization algorithm is presented that integrates function predictions from different Gene Ontology (GO) terms to a single prediction that is consistent to the True Path Rule as imposed by the GO Directed Acyclic Graph. This integration leads to predictions that are easy to be interpreted. Evaluation of of this algorithm using Arabidopsis data showed that the prediction performance is improved, compared to single GO term predictions.

    Bayesian networks for omics data analysis
    Gavai, A.K. - \ 2009
    Wageningen University. Promotor(en): Jack Leunissen; Michael Muller, co-promotor(en): Guido Hooiveld; P.J.F. Lucas. - [S.l.] : S.n. - ISBN 9789085853909 - 98
    bio-informatica - waarschijnlijkheidsmodellen - bayesiaanse theorie - netwerkanalyse - genexpressie - roken - vluchtige verbindingen - biochemische omzettingen - voedingsonderzoek bij de mens - genexpressieanalyse - microarrays - netwerken - nutrigenomica - bioinformatics - probabilistic models - bayesian theory - network analysis - gene expression - smoking - volatile compounds - biochemical pathways - human nutrition research - genomics - microarrays - networks - nutrigenomics
    This thesis focuses on two aspects of high throughput technologies, i.e. data storage and data analysis, in particular in transcriptomics and metabolomics. Both technologies are part of a research field that is generally called ‘omics’ (or ‘-omics’, with a leading hyphen), which refers to genomics, transcriptomics, proteomics, or metabolomics. Although these techniques study different entities (genes, gene expression, proteins, or metabolites), they all have in common that they use high-throughput technologies such as microarrays and mass spectrometry, and thus generate huge amounts of data. Experiments conducted using these technologies allow one to compare different states of a living cell, for example a healthy cell versus a cancer cell or the effect of food on cell condition, and at different levels.
    The tools needed to apply omics technologies, in particular microarrays, are often manufactured by different vendors and require separate storage and analysis software for the data generated by them. Moreover experiments conducted using different technologies cannot be analyzed simultaneously to answer a biological question. Chapter 3 presents MADMAX, our software system which supports storage and analysis of data from multiple microarray platforms. It consists of a vendor-independent database which is tightly coupled with vendor-specific analysis tools. Upcoming technologies like metabolomics, proteomics and high-throughput sequencing can easily be incorporated in this system.
    Once the data are stored in this system, one obviously wants to deduce a biological relevant meaning from these data and here statistical and machine learning techniques play a key role. The aim of such analysis is to search for relationships between entities of interest, such as genes, metabolites or proteins. One of the major goals of these techniques is to search for causal relationships rather than mere correlations. It is often emphasized in the literature that "correlation is not causation" because people tend to jump to conclusions by making inferences about causal relationships when they actually only see correlations. Statistics are often good in finding these correlations; techniques called linear regression and analysis of variance form the core of applied multivariate statistics. However, these techniques cannot find causal relationships, neither are they able to incorporate prior knowledge of the biological domain. Graphical models, a machine learning technique, on the other hand do not suffer from these limitations.
    Graphical models, a combination of graph theory, statistics and information science, are one of the most exciting things happening today in the field of machine learning applied to biological problems (see chapter 2 for a general introduction). This thesis deals with a special type of graphical models known as probabilistic graphical models, belief networks or Bayesian networks. The advantage of Bayesian networks over classical statistical techniques is that they allow the incorporation of background knowledge from a biological domain, and that analysis of data is intuitive as it is represented in the form of graphs (nodes and edges). Standard statistical techniques are good in describing the data but are not able to find non-linear relations whereas Bayesian networks allow future prediction and discovering nonlinear relations. Moreover, Bayesian networks allow hierarchical representation of data, which makes them particularly useful for representing biological data, since most biological processes are hierarchical by nature. Once we have such a causal graph made either by a computer program or constructed manually we can predict the effects of a certain entity by manipulating the state of other entities, or make backward inferences from effects to causes. Of course, if the graph is big, doing the necessary calculations can be very difficult and CPU-expensive, and in such cases approximate methods are used.
    Chapter 4 demonstrates the use of Bayesian networks to determine the metabolic state of feeding and fasting mice to determine the effect of a high fat diet on gene expression. This chapter also shows how selection of genes based on key biological processes generates more informative results than standard statistical tests. In chapter 5 the use of Bayesian networks is shown on the combination of gene expression data and clinical parameters, to determine the effect of smoking on gene expression and which genes are responsible for the DNA damage and the raise in plasma cotinine levels of blood of a smoking population. This study was conducted at Maastricht University where 22 twin smokers were profiled. Chapter 6 presents the reconstruction of a key metabolic pathway which plays an important role in ripening of tomatoes, thus showing the versatility of the use of Bayesian networks in metabolomics data analysis.
    The general trend in research shows a flood of data emerging from sequencing and metabolomics experiments. This means that to perform data mining on these data one requires intelligent techniques that are computationally feasible and able to take the knowledge of experts into account to generate relevant results. Graphical models fit this paradigm well and we expect them to play a key role in mining the data generated from omics experiments.
    Zijn biologische boeren minder risicomijdend dan gangbare boeren?
    Gardebroek, C. - \ 2008
    Stator, periodiek van VVS 9 (2008)4. - ISSN 1567-3383 - p. 9 - 13.
    biologische landbouw - bedrijfssystemen - risico - bayesiaanse theorie - schattingen - risicoanalyse - organic farming - farming systems - risk - bayesian theory - estimates - risk analysis
    Het toepassen van biologische landbouwmethodes is voor individuele boeren in een aantal opzichten risicovoller dan het gebruik van gangbare productietechnieken. Dit suggereert dat biologische boeren meer bereid zijn risico's te accepteren dan hun gangbare collega's. Om dit te onderzoeken zijn met behulp van Bayesiaanse schattingstechnieken en panelgegevens individuele risicocoëfficiënten geschat voor biologische en gangbare boeren.
    Flexible decision-making in crisis events : discovering real options in the control of foot-and-mouth disease epidemics
    Ge, L. - \ 2008
    Wageningen University. Promotor(en): Ruud Huirne, co-promotor(en): A.R. Kristensen; Monique Mourits. - [S.l.] : S.n. - ISBN 9789085049692 - 149
    crises - mond- en klauwzeer - epidemieën - besluitvorming - ziektebestrijding - markov-processen - onzekerheid - dynamisch programmeren - bayesiaanse theorie - dynamische modellen - bedrijfseconomie - beslissingsondersteunende systemen - beslissingsmodellen - crises - foot and mouth disease - epidemics - decision making - disease control - markov processes - uncertainty - dynamic programming - bayesian theory - dynamic models - business economics - decision support systems - decision models

    Keywords
    Crisis event, foot-and-mouth disease (FMD), epidemic control, real options, decision flexibility, multi-level hierarchic Markov process (MLHMP), uncertainty, decision-support framework, turning moment, dynamic programming, Bayesian forecasting, dynamic models, overreacting, underreacting

    This research introduced the real options way of thinking into decision-making in crisis events like animal epidemics, with foot-and-mouth disease (FMD) as a case in point. A unique angle was taken to investigate decision flexibility in choosing optimal control strategies. The main objective was to develop a flexible decision-support framework which corresponds to practice and provides consistent treatment of ongoing uncertainty in controlling animal epidemics. Conceptualisation and operationalisation of decision flexibility were the two main focuses.
    A decision analysis revealed the dynamic and sequential nature of decision- making in the control of animal epidemics. The importance of decision flexibility was attributed to the existence of uncertainty and linked decisions in the multi-stage decision process. Timing of control options and the possibility of learning were found to be essential in conceptualising decision flexibility. To operationalise decision flexibility, the main methodological approach was the integration of multi-level hierarchic Markov process (MLHMP) and Bayesian forecasting methods. Based on MLHMP and dynamic generalised linear models (DGLM), a new decision-support framework was developed to investigate the impact of uncertainty and the possibility of learning in choosing the optimal timing of control options over time. The framework paid special attention to the interdependency among strategic, tactical, and operational decisions in managing FMD epidemics. The decision-support framework was shown to be useful in contingency planning for future epidemics.
    Addressing the decision flexibility in a dynamic decision process, real options analysis and MLHMP were found to be complementary in developing the flexible decision-support framework. Both required dynamic assessment of future epidemic development and control options. Towards empirical application of the decision-support framework, an integrated epidemic-economic modelling approach was described and illustrated with simulated epidemics. It was shown that, by including decision flexibility in the dynamic decision process of epidemic control, the new modelling approach enabled more realistic estimation of the costs of underreacting or overreacting than the traditional static approaches.

    Valuation of land use in the Netherlands and British Columbia: a spatial hedonic GIS-based approach
    Cotteleer, G. - \ 2008
    Wageningen University. Promotor(en): Arie Oskam; Kees van Kooten, co-promotor(en): Jack Peerlings. - [S.l.] : S.n. - ISBN 9789085049470 - 158
    landgebruik - open ruimten - landgebruiksplanning - relaties tussen stad en platteland - landbouwgrond - geografische informatiesystemen - economische evaluatie - agrarische economie - econometrie - econometrische modellen - eigendomsoverdrachten - niet marktbare baten - bayesiaanse theorie - nederland - canada - parttime landbouwbedrijven - ruimtelijke analyse - regionale economie - ruimtelijke economie - ruimtelijke modellen - land use - open spaces - land use planning - rural urban relations - agricultural land - geographical information systems - economic evaluation - agricultural economics - econometrics - econometric models - property transfers - non-market benefits - bayesian theory - netherlands - canada - part time farming - spatial analysis - regional economics - spatial economics - spatial models
    The main reason for government intervention in land markets is market failure. Open space is a non-market output or externality of farmland and, although it might be important to people, there is no actual market for the good as such. The Netherlands and the Province of British Columbia in Canada both experience similar problems of expanding cities and pressure on open space, and they both use zoning to regulate land use and its externalities. The objective of this research is to evaluate the effect of zoning on the preservation of open space in the urban-rural fringe and to quantify the externalities that different types of land use impose on residential properties
    Bayesian classification of vegetation types with Gaussian mixture density fitting to indicator values
    Witte, J.P.M. ; Wójcik, R. ; Torfs, P.J.J.F. ; Haan, M.W.H. ; Hennekens, S.M. - \ 2007
    Journal of Vegetation Science 18 (2007)4. - ISSN 1100-9233 - p. 605 - 612.
    vegetatietypen - indicatorsoorten - bayesiaanse theorie - vegetation types - indicator species - bayesian theory - functional traits - moisture - ecology - tool
    Question: Is it possible to mathematically classify relevés into vegetation types on the basis of their average indicator values, including the uncertainty of the classification? Location: The Netherlands. Method: A large relevé database was used to develop a method for predicting vegetation types based on indicator values. First, each relevé was classified into a phytosociological association on the basis of its species composition. Additionally, mean indicator values for moisture, nutrients and acidity were computed for each relevé. Thus, the position of each classified relevé was obtained in a three-dimensional space of indicator values. Fitting the data to so called Gaussian Mixture Models yielded densities of associations as a function of indicator values. Finally, these density functions were used to predict the Bayesian occurrence probabilities of associations for known indicator values. Validation of predictions was performed by using a randomly chosen half of the database for the calibration of densities and the other half for the validation of predicted associations. Results and Conclusions: With indicator values, most relevés were classified correctly into vegetation types at the association level. This was shown using confusion matrices that relate (1) the number of relevés classified into associations based on species composition to (2) those based on indicator values. Misclassified relevés belonged to ecologically similar associations. The method seems very suitable for predictive vegetation models.
    Integration of three strucutally different stock assessment models in a Bayesian framework
    Kraak, S.B.M. ; Bogaards, H. ; Borges, L. ; Machiels, M.A.M. ; Keeken, O.A. van - \ 2007
    IJmuiden : IMARES (Report / Wageningen IMARES C043/07) - 7
    vissen - beoordeling - schattingen - bayesiaanse theorie - voorspellen - modellen - visstand - fishes - assessment - estimates - bayesian theory - forecasting - models - fish stocks
    Bayesian statistics provide a method for expressing uncertainty of an unknown parameter value probabilistically (www.bayesian.org). Bayesian methods have been widely used in biological sciences, and recently in fisheries science applied to stock assessment. In our previous studies on Bayesian analysis for the F-project, we have explored three structurally different stock assessment models in a Bayesian framework. These models are not only different with respect to their data needs, they also represent different hypotheses about the stock dynamics.
    2006 stock assessment of North Sea plaice using a Bayesian catch-at-age model
    Borges, L. ; Kraak, S.B.M. ; Bogaards, J.J.P. ; Machiels, M.A.M. - \ 2007
    IJmuiden : IMARES (Report / IMARES C034/07) - 19
    vis vangen - noordzee - schol - beoordeling - schatting - bayesiaanse theorie - onzekerheid - visstand - fishing - north sea - plaice - assessment - estimation - bayesian theory - uncertainty - fish stocks
    Projectverslag over de verbetering van de toestandsbeoordeling van schol en tong. Problemen ronde de onzekerheid en bias in de toestandsbeoordeling en de gegevens die daar voor worden gebruikt zijn onderzocht. Dit verslag betreft de onzekerheid in de toestandsbeoordeling van Noordzee schol aan de hand van een Bayesiaans 'catch at age'- model (vangst per leeftijd).
    Bayesian analysis of research vessel surveys: trends in North Sea plaice abundance
    Bogaards, J.J.P. ; Borges, L. ; Machiels, M.A.M. ; Kraak, S.B.M. - \ 2007
    IJmuiden : IMARES (Report / Wageningen IMARES C033/07) - 25
    schol - noordzee - beoordeling - bayesiaanse theorie - statistische analyse - visstand - plaice - north sea - assessment - bayesian theory - statistical analysis - fish stocks
    Trendwatch combining expert opinion
    Hendrix, E.M.T. ; Kornelis, M. ; Pegge, S.M. ; Galen, M.A. van - \ 2006
    Wageningen : Agrotechnology & Food Sciences Group (Rapport / Agrotechnology & Food Sciences Group 622) - ISBN 9789085850113 - 31
    opinies - deskundigen - bayesiaanse theorie - modellen - entropie - tendensen - gegevensanalyse - gevalsanalyse - opinions - experts - bayesian theory - models - entropy - trends - data analysis - case studies
    In this study, focus is on a systematic way to detect future changes in trends that may effect the dynamics in the agro-food sector, and on the combination of opinions of experts. For the combination of expert opinions, the usefulness of multilevel models is investigated. Bayesian data analysis is used to obtain parameter estimates. The approach is illustrated by two case studies. The results are promising, but the procedures are just a first step into an appropriate combination of expert combination, which has to be completed on important issues, such as the identification of some well-known biases.
    Calibration in a Bayesian modelling framework
    Jansen, M.J.W. ; Hagenaars, T.H.J. - \ 2004
    In: Bayesian Statistics and Quality Modelling in the Agro-Food Production Chain / Boekel, van, Stein, A., Bruggen, van, Dordrecht : Kluwer (Wageningen UR Frontis series vol. 3) - ISBN 9781402019166 - p. 47 - 55.
    bayesiaanse theorie - monte carlo-methode - wiskundige modellen - kalibratie - onzekerheid - beslissingsondersteunende systemen - bayesian theory - monte carlo method - mathematical models - calibration - uncertainty - decision support systems
    Bayesian statistics may constitute the core of a consistent and comprehensive framework for the statistical aspects of modelling complex processes that involve many parameters whose values are derived from many sources. Bayesian statistics holds great promises for model calibration, provides the perfect starting point for uncertainty analysis and provides an excellent starting point for decision support. The purpose of this paper is to draw attention to problems and possible solutions. It is not our intention to introduce ready-for-use methods
    Bayesian statistics for infection experiments
    Heres, L. ; Engel, B. - \ 2004
    In: Bayesian Statistics and Quality Modelling in the Agro-Food Production Chain / Boekel, van, Stein, A., Bruggen, van, Dordrecht : Kluwer (Wageningen UR Frontis series vol. 3) - ISBN 9781402019166 - p. 131 - 139.
    bayesiaanse theorie - markov-processen - monte carlo-methode - ziekten overgebracht door voedsel - pathogenen - pluimvee - epidemiologie - bayesian theory - markov processes - monte carlo method - foodborne diseases - pathogens - poultry - epidemiology
    To intervene cycles of food-borne pathogens in poultry new intervention methods need to be tested for their effectiveness. In this paper a statistical method is described that was applied to quantify the observed differences between test groups and control groups. Treated chickens and their controls were inoculated with several doses and were daily examined for the shedding of the tested pathogens. For these infection experiments with individually housed chickens and where binary data were available for each individual chicken a Bayesian analysis employing Markov Chain Monte Carlo (MCMC) was applied for the statistical analyses. The Cox’ proportional hazard reflected the typical features of the data, i. e. dependency, waitingtime structure and censoring. The outcomes of the analyses are two measures of difference in susceptibility between the feed groups. The first effect measure is a relative risk of being infected. The second is a difference in waiting time or a difference in inoculation dose to get a comparable proportion of infected animals
    Bayesian solutions for food science problems?
    Boekel, M.A.J.S. van - \ 2004
    In: Bayesian statistics and Quality Modelling in the Agro-Food Production Chain / van Boekel, M.A.J.S., Stein, A., van Bruggen, A., Dordrecht : Kluwer (Wageningen Frontis Series 3) - ISBN 9781402019166 - p. 17 - 27.
    voedselkwaliteit - bayesiaanse theorie - wiskundige modellen - kinetica - voedselveiligheid - food quality - bayesian theory - mathematical models - kinetics - food safety
    This paper starts with an overview of some typical food-science problems. In view of the development of safe and healthy food, the use of mathematical models in food science is much needed and the use of statistics is therefore indispensable. Because of the biological variability in the raw materials on the one hand and the complex nature of foods on the other hand food-science problems are characterized by a high degree of uncertainty as well as variability. Consequently, when dealing with these problems Bayesian statistics could be very helpful; however, it is hardly used at all. This paper discusses some possible applications concerning the modelling of food quality and food safety. It is concluded that a Bayesian approach could be quite useful and its potential should be further explored in future research
    Bayesian modelling of dietary intake of pesticides using monitoring and survey data
    Paulo, M.J. ; Voet, H. van der - \ 2004
    Wageningen : Plant Research International (Biometris : quantitative methods in life and earth sciences )
    voedselopname - pesticiden - bayesiaanse theorie - gegevensanalyse - voedingsverkenningen - monitoring - food intake - pesticides - bayesian theory - data analysis - nutrition surveys
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