A new approach to Bayesian inference, named the prior-free inference, is introduced for developing objective Bayesian analysis based on information-theoretic approach. This new approach is essentially a Bayesian method but it does not depend on a prior distribution for unknown parameters. Thus, this approach not only has the advantages of the Bayesian approach but also can avoid the difficulty,...

Bayesian inference plays an important role in phylogenetics, evolutionary biology, and in many other branches of science. It provides a principled framework for dealing with uncertainty and quantifying how it changes in the light of new evidence. For many complex models and inference problems, however, only approximate quantitative answers are obtainable. Approximate Bayesian computation (ABC) ...

Bayesian networks (BNs) are a key method for representation and reasoning under uncertainty in artificial intelligence. Both exact and approximate BN inference have been proven to be NP-hard. The problems of inference become even less tractable under real-time constraints. One solution to real-time AI problems is to develop anytime algorithms. Anytime algorithms are iterative refinement algorit...

This is an expository paper dealing with Bayesian inference for three important mixture problems in the area of quality and reliability. The traditional approach for estimation in these situations is the method of maximum likelihood. The corresponding inference based on large-sample theory can, however, be misleading in situations where the likelihood cannot be well approximated by the normal d...

Approximate Bayesian inference is NP-hard. Dagum and Luby defined the Local Variance Bound (LVB) to measure the approximation hardness of Bayesian inference on Bayesian networks, assuming the networks model strictly positive joint probability distributions, i.e. zero probabilities are not permitted. This paper introduces the k-test to measure the approximation hardness of inference on Bayesian ...

The fault prediction model grants assistance during the software development by providing recourse to the present faults with the Bayesian Interference. All faults prediction techniques get a help in this study with the designing of Logistic regression model and Bayesian inference altogether. It is also told as fact that Bayesian inference graph can be represented for probabilistic approach for...

This paper presents a review of Bayesian models of brain and behaviour. We first review the basic principles of Bayesian inference. This is followed by descriptions of sampling and variational methods for approximate inference, and forward and backward recursions in time for inference in dynamical models. The review of behavioural models covers work in visual processing, sensory integration, se...

Many of the mathematical frameworks describing natural selection are equivalent to Bayes' Theorem, also known as Bayesian updating. By definition, a process of Bayesian Inference is one which involves a Bayesian update, so we may conclude that these frameworks describe natural selection as a process of Bayesian inference. Thus, natural selection serves as a counter example to a widely-held inte...

Variational Bayesian inference and (collapsed) Gibbs sampling are the two important classes of inference algorithms for Bayesian networks. Both have their advantages and disadvantages: collapsed Gibbs sampling is unbiased but is also inefficient for large count values and requires averaging over many samples to reduce variance. On the other hand, variational Bayesian inference is efficient and ...

Abstract – A particle filter is applied to the problem of detecting and tracking multiple sound sources by Bayesian inference using combined audio and video information. The problem is formulated within a general framework of Bayesian hidden variable sequence estimation by fusing observed information. The particle filter is then introduced as an approximation of Bayesian inference. Experiments ...