Bayesian adaptation in Poisson cognitive systems

Bayesian change point estimation in Poisson-based control charts

Precise identification of the time when a process has changed enables process engineers to search for a potential special cause more effectively. In this paper, we develop change point estimation methods for a Poisson process in a Bayesian framework. We apply Bayesian hierarchical models to formulate the change point where there exists a step < /div> change, a linear trend and a known multip...

Bayesian Nonparametric Poisson Factorization for Recommendation Systems

We develop a Bayesian nonparametric Poisson factorization model for recommendation systems. Poisson factorization implicitly models each user’s limited budget of attention (or money) that allows consumption of only a small subset of the available items. In our Bayesian nonparametric variant, the number of latent components is theoretically unbounded and effectively estimated when computing a po...

Bayesian paradigm for analysing count data in longitudina studies using Poisson-generalized log-gamma model

In analyzing longitudinal data with counted responses, normal distribution is usually used for distribution of the random efffects. However, in some applications random effects may not be normally distributed. Misspecification of this distribution may cause reduction of efficiency of estimators. In this paper, a generalized log-gamma distribution is used for the random effects which includes th...

Bayesian Columnar Networks for Grounded Cognitive Systems

There are two elementary building blocks whose intimate relationship has been overlooked so far: The cortical column as ubiquitous local processor underlying all cognitive operations and the Bayesian framework with the generic operation of calculating posterior probabilities given likelihoods and prior probabilities. In this contribution we try to bridge this gap by providing a detailed mapping...

Comparative Adaptation of some pox viruses in two cell systems