Bayesian statistics has become a popular framework in various fields of experimental psychology such as signal detection theory, speech recognition, cue integration and decision making. However, it is still an open question how the human brain actually incorporates this functionality. One assumption is that the activities of populations of neurons encode probability distributions. Indeed, it ha...

A Bayesian optimization algorithm for the nurse scheduling problem is presented, which involves choosing a suitable scheduling rule from a set for each nurse’s assignment. Unlike our previous work that used GAs to implement implicit learning, the learning in the proposed algorithm is explicit, i.e. eventually, we will be able to identify and mix building blocks directly. The Bayesian optimizati...

A Bayesian optimization algorithm for the nurse scheduling problem is presented, which involves choosing a suitable scheduling rule from a set for each nurse’s assignment. Unlike our previous work that used GAs to implement implicit learning, the learning in the proposed algorithm is explicit, i.e. eventually, we will be able to identify and mix building blocks directly. The Bayesian optimizati...

This paper develops multiple-prior Bayesian inference for a set-identi…ed parameter whose identi…ed set is constructed by an intersection of two identi…ed sets. We formulate an econometrician’s practice of "adding an assumption" as "updating ambiguous beliefs." Among several ways to update ambiguous beliefs proposed in the literature, we consider the DempsterShafer updating rule (Dempster (1968...

This paper reviews the past and current interest in using Bayesian thinking to introduce statistical inference. Rationale for using a Bayesian approach is described and particular methods are described that make it easier to understand Bayes' rule. Several older and modern introductory statistics books are reviewed that use a Bayesian perspective. It is argued that a Bayesian perspective is ver...

Uncertainty is omnipresent when we perceive or interact with our environment, and the Bayesian framework provides computational methods for dealing with it. Mathematical models for Bayesian decision making typically require datastructures that are hard to implement in neural networks. This article shows that even the simplest and experimentally best supported type of synaptic plasticity, Hebbia...