In Templeton (2010), the Approximate Bayesian Computation (ABC) algorithm (see, e.g., Pritchard et al., 1999, Beaumont et al., 2002, Marjoram et al., 2003, Ratmann et al., 2009) is criticised on mathematical and logical grounds: “the [Bayesian] inference is mathematically incorrect and formally illogical”. Since those criticisms turn out to be bearing on Bayesian foundations rather than on the ...

Within decision theory the use of Bayesian methodology is well established. However, primarily due to complexity problems the use in applied Decision Support Systems within animal production has been very limited. Recently, methodological developments have removed many of these problems, and the time seems to be ripe for wide-scale applications. Bayesian methods handles decision making under un...

Companies strive to improve their business processes in order to remain competitive. Process mining aims to infer meaningful insights from process-related data and attracted the attention of practitioners, tool-vendors, and researchers in recent years. Traditionally, event logs are assumed to describe the as-is situation. But this is not necessarily the case in environments where logging may be...

Bayesian networks model conditional dependencies among the domain variables, and provide a way to deduce their interrelationships as well as a method for the classification of new instances. One of the most challenging problems in using Bayesian networks, in the absence of a domain expert who can dictate the model, is inducing the structure of the network from a large, multivariate data set. We...

In this paper, Spike-and-Slab Dirichlet Process (SS-DP) priors are introduced and discussed for non-parametric Bayesian modeling and inference, especially in the mixture models context. Specifying a spike-and-slab base measure for DP priors combines the merits of Dirichlet process and spike-and-slab priors and serves as a flexible approach in Bayesian model selection and averaging. Computationa...

This paper proposes a dynamic Bayesian network to represent the cause-and-effect relationships in an industrial supply chain. Based on the Quick Scan, a systematic data analysis and synthesis methodology developed by Naim, Childerhouse, Disney, and Towill (2002). [A supply chain diagnostic methodlogy: Determing the vector of change. Computers and Industrial Engineering, 43, 135–157], a dynamic ...

A participatory integrated (social, economic, environmental) approach based on causal loop diagram, Bayesian belief networks and evolutionary multiobjective optimisation is proposed for efficient water resources management. The proposed methodology incorporates all the conflicting objectives in the decision making process. Causal loop diagram allows a range of different factors to be considered...

We propose a fully Bayesian approach for generalized kernel models (GKMs), which are extensions of generalized linear models in the feature space induced by a reproducing kernel. We place a mixture of a point-mass distribution and Silverman’s g-prior on the regression vector of GKMs. This mixture prior allows a fraction of the regression vector to be zero. Thus, it serves for sparse modeling an...

In this paper, the theoretical foundations of generalised fuzzy Bayesian Network based on Vectorial Centroid defuzzification is introduced. The extension of Bayesian Network takes a broad view by examples labelled by a fuzzy set of attributes, instead of a classical set. Combining fuzzy set theory and Bayesian Network’s knowledge allows the use of fuzzy variables or attributes that widely used ...