Given a few assumptions, the probability of a conjunction is raised, and the probability of its negation is lowered, by conditionalising upon one of the conjuncts. This simple result appears to bring Bayesian confirmation theory into tension with the prominent dogmatist view of perceptual justification – a tension often portrayed as a kind of ‘Bayesian objection’ to dogmatism. In a recent paper...

We describe the results of performing data mining on a challenging medical diagnosis domain, acute abdominal pain. This domain is well known to be diicult, yielding little more than 60% pre-dictive accuracy for most human and machine di-agnosticians. Moreover, many researchers argue that one of the simplest approaches, the naive Bayesian classiier, is optimal. By comparing the performance of th...

This paper conducts a Bayesian analysis of inverse Gaussian process models for degradation modeling and inference. Novel features of the Bayesian analysis are the natural manners for incorporating subjective information, pooling of random effects information among product population, and a straightforward way of coping with evolving data sets for on-line prediction. A general Bayesian framework...

People can behave in a way that is consistent with Bayesian models of cognition, despite the fact that performing exact Bayesian inference is computationally challenging. What algorithms could people be using to make this possible? We show that a simple sequential algorithm "Win-Stay, Lose-Sample", inspired by the Win-Stay, Lose-Shift (WSLS) principle, can be used to approximate Bayesian infere...

In this paper we propose the use of Bayesian Networks as a theoretical framework for Computerized Adaptive Tests. To this end, we develop the Bayesian Network that supports the Adaptive Testing Algorithm, that is, we define what variables should be taken into account, what kind of relationships should be established among them, and what are the required parameters. As parameter specification is...

We propose simple transformation of the hidden states in variational Bayesian factor analysis models to speed up the learning procedure. The speed-up is achieved by using proper parameterization of the posterior approximation which allows joint optimization of its individual factors, thus the transformation is theoretically justified. We derive the transformation formulae for variational Bayesi...

Bayesian networks provide a method of rep­ resenting conditional independence between random variables and computing the prob­ ability distributions associated with these random variables. In this paper, we ex­ tend Bayesian network structures to compute probability density functions for continuous random variables. We make this extension by approximating prior and conditional den­ sities using...

The Markov Blanket Bayesian Classifier is a recentlyproposed algorithm for construction of probabilistic classifiers. This paper presents an empirical comparison of the MBBC algorithm with three other Bayesian classifiers: Naïve Bayes, Tree-Augmented Naïve Bayes and a general Bayesian network. All of these are implemented using the K2 framework of Cooper and Herskovits. The classifiers are comp...

We introduce a simple, general framework for likelihood-free Bayesian reinforcement learning, through Approximate Bayesian Computation (ABC). The advantage is that we only require a prior distribution on a class of simulators. This is useful when a probabilistic model of the underlying process is too complex to formulate, but where detailed simulation models are available. ABC-RL allows the use...

We derive an exact and efficient Bayesian regression algorithm for piecewise constant functions of unknown segment number, boundary location, and levels. It works for any noise and segment level prior, e.g. Cauchy which can handle outliers. We derive simple but good estimates for the in-segment variance. We also propose a Bayesian regression curve as a better way of smoothing data without blurr...