Abstract Surprise-based learning allows agents to rapidly adapt nonstationary stochastic environments characterized by sudden changes. We show that exact Bayesian inference in a hierarchical model gives rise surprise-modulated trade-off between forgetting old observations and integrating them with the new ones. The modulation depends on probability ratio, which we call Bayes Factor Surprise, te...

To compare different QTL mapping methods, a population with genotypic and phenotypic data was simulated. In Bayesian approach, all information of markers can be used along with combination of distributions of SNP markers. It is assumed that most of the markers (95%) have minor effects and a few numbers of markers (5%) exert major effects. The simulated population included a basic population of ...

In a classical parametric setup, a key factor in the implementation of the Empirical Bayes methodology is the incorporation of a suitable prior that is compatible with the parametric setup and yet lends to the estimation of the Bayes (shrinkage) factor in an empirical manner. The situation is more complex in semi-parametric and (ev,:,n more in) nonparametric models. Although the Dirichlet prior...

Clustering by maximizing the dependency between (margin) groupings or partitionings of co-occurring data pairs is studied. We suggest a probabilistic criterion that generalizes discriminative clustering (DC), an extension of the information bottleneck (IB) principle to labeled continuous data. The criterion is the Bayes factor between models assuming dependence and independence of the two clust...

Abstract What have been called ‘Bayesian confirmation measures’ or ‘evidential support offer a numerical expression for the impact of piece evidence on judicial hypothesis interest. The Bayes’ factor, sometimes simply ‘likelihood ratio’, represents best measure value evidence. It satisfies number necessary conditions normative logical adequacy. is shown that same cannot be said alternative expr...

In this study, we examined the current practice and alternative methods for interpreting nonsignificant findings in psychological research. The traditional null-hypothesis testing presents a challenge for researchers to interpret nonsignificant findings. We reviewed the abstracts of all empirical articles published in three high-esteem psychological journals in 2015 and selected those which ref...