Approximate inference for Bayesian models is dominated by two approaches, variational Bayesian inference and Markov Chain Monte Carlo. Both approaches have their own advantages and disadvantages, and they can complement each other. Recently researchers have proposed collapsed variational Bayesian inference to combine the advantages of both. Such inference methods have been successful in several...

Bayesian Data Analysis. Bayesian inference is too narrow; Bayesian statistics is too broad. Bayes is a good brand name; Statistics using conditional. Bayesian Data Analysis: Straightline fitting. Stephen F. Gull. Cavendish Laboratory,. Madingley Road,. Cambridge CB3 OHE, U.K Abstract. A Bayesian Overview. Bayesian data analysis. John K. Kruschke. . Bayesian methods have garnered huge interest i...

1 Goals and Outline Throughout this book, the topic of order-restricted inference is dealt with almost exclusively from a Bayesian perspective. Some readers may wonder why the other main school for statistical inference – frequentist inference – has received so little attention here. Isn't it true that in the field of psychology, almost all inference is frequentist inference? The first goal of ...

This paper presents a collapsed variational Bayesian inference algorithm for PCFGs that has the advantages of two dominant Bayesian training algorithms for PCFGs, namely variational Bayesian inference and Markov chain Monte Carlo. In three kinds of experiments, we illustrate that our algorithm achieves close performance to the Hastings sampling algorithm while using an order of magnitude less t...

We propose a simple and effective variational inference algorithm based on stochastic optimisation that can be widely applied for Bayesian non-conjugate inference in continuous parameter spaces. This algorithm is based on stochastic approximation and allows for efficient use of gradient information from the model joint density. We demonstrate these properties using illustrative examples as well...

As Bayesian networks are applied to more complex and realistic real-world applications, the development of more efficient inference algorithms working under real-time constraints is becoming more and more important. This paper presents a survey of various exact and approximate Bayesian network inference algorithms. In particular, previous research on real-time inference is reviewed. It provides...

This paper suggests one method to process fMRI time series based on Bayesian inference for group analysis. The method is based on Bayesian inference to divide group into multilevel by session, subject and group levels. It compares covariance to select prior to reinforce posterior probability in group analysis. At the same time it combines classical statistics, i.e., t-statistics to obtain voxel...