Current refinement(s):
An economic approach to nonanimal toxicity testing for skin sensitisation Leontaridou, Maria  \ 2017
Wageningen University. Promotor(en): E.C. van Ierland, copromotor(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 nonanimal 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 nonanimal 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 resourceefficiency in the development of testing strategies. Chapter 3 extends to the development of a Bayesian ValueofInformation model for the optimisation of nonanimal toxicity testing strategies. This optimisation model is applied to construct optimal nonanimal 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 clearcut 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 nonanimal 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 nonanimal 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 resourceefficiency 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, copromotor(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, copromotor(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 depthdependence 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 lead210 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, copromotor(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, nondeterministic and semistructured 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 realtime. 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, copromotor(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. 

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 traitbased 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 traitenvironment relationships in the context of species distribution modelling, so in the context of speciesenvironment 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 modelbased 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 betweenspecies variation in response to the environment. This socalled generalized linear mixed model (GLMM)is derived byintegrating a twostep 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 traitenvironment 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 presenceabsence data, the simplest unimodal (nonnegative) 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 traitmodulated 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 (TypeII 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 highdimensional data problems. By further study the multitrait and multienvironmental 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 nonsignificant terms are removed based on a modified Akaike information criterion. The linear mixed model with the tiered forward selection is compared with TypeII ML and existing methods for detecting traitenvironment relationships that are not based on mixed models, namely the fourth corner method and the linear traitenvironment method (LTE). 

Bayesian Markov random field analysis for integrated networkbased protein function prediction Kourmpetis, Y.I.A.  \ 2011
Wageningen University. Promotor(en): Cajo ter Braak, copromotor(en): Roeland van Ham.  [S.l.] : S.n.  ISBN 9789085859598  113 statistiek  bayesiaanse theorie  markovprocessen  netwerkanalyse  biostatistiek  toegepaste statistiek  bioinformatica  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, copromotor(en): Guido Hooiveld; P.J.F. Lucas.  [S.l.] : S.n.  ISBN 9789085853909  98 bioinformatica  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 highthroughput 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 vendorindependent database which is tightly coupled with vendorspecific analysis tools. Upcoming technologies like metabolomics, proteomics and highthroughput 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 nonlinear 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 CPUexpensive, 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 15673383  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 decisionmaking in crisis events : discovering real options in the control of footandmouth disease epidemics Ge, L.  \ 2008
Wageningen University. Promotor(en): Ruud Huirne, copromotor(en): A.R. Kristensen; Monique Mourits.  [S.l.] : S.n.  ISBN 9789085049692  149 crises  mond en klauwzeer  epidemieën  besluitvorming  ziektebestrijding  markovprocessen  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 This research introduced the real options way of thinking into decisionmaking in crisis events like animal epidemics, with footandmouth 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 decisionsupport 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. 

Valuation of land use in the Netherlands and British Columbia: a spatial hedonic GISbased approach Cotteleer, G.  \ 2008
Wageningen University. Promotor(en): Arie Oskam; Kees van Kooten, copromotor(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  nonmarket 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 nonmarket 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 urbanrural 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 11009233  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 threedimensional 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 Fproject, 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 catchatage 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 agrofood 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 wellknown biases.


Calibration in a Bayesian modelling framework Jansen, M.J.W. ; Hagenaars, T.H.J.  \ 2004
In: Bayesian Statistics and Quality Modelling in the AgroFood Production Chain / Boekel, van, Stein, A., Bruggen, van, Dordrecht : Kluwer (Wageningen UR Frontis series vol. 3)  ISBN 9781402019166  p. 47  55. bayesiaanse theorie  monte carlomethode  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 readyforuse methods


Bayesian statistics for infection experiments Heres, L. ; Engel, B.  \ 2004
In: Bayesian Statistics and Quality Modelling in the AgroFood Production Chain / Boekel, van, Stein, A., Bruggen, van, Dordrecht : Kluwer (Wageningen UR Frontis series vol. 3)  ISBN 9781402019166  p. 131  139. bayesiaanse theorie  markovprocessen  monte carlomethode  ziekten overgebracht door voedsel  pathogenen  pluimvee  epidemiologie  bayesian theory  markov processes  monte carlo method  foodborne diseases  pathogens  poultry  epidemiology
To intervene cycles of foodborne 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 AgroFood 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 foodscience 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 foodscience 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
