Recently, significant progress has been made developing kernel mean expressions for Bayesian inference. An important success in this domain is the nonparametric kernel Bayes’ filter (nKB-filter), which can be used for sequential inference in state space models. We expand upon this work by introducing a smoothing algorithm, the nonparametric kernel Bayes’ smoother (nKB-smoother) which relies on ...

We introduce a framework for decision making in which the learning of decision making is reduced to its simplest and biologically most plausible form: Hebbian learning on a linear neuron. We cast our Bayesian-Hebb learning rule as reinforcement learning in which certain decisions are rewarded and prove that each synaptic weight will on average converge exponentially fast to the log-odd of recei...

In the 1960s, Shiryaev developed a Bayesian theory of change-point detection in the i.i.d. case, which was generalized in the beginning of the 2000s by Tartakovsky and Veeravalli for general stochastic models assuming a certain stability of the log-likelihood ratio process. Hidden Markov models represent a wide class of stochastic processes that are very useful in a variety of applications. In ...

I propose a normative updating rule, extended Bayesianism, for the incorporation of probabilistic information arising from process becoming more aware. Extended Bayesianism generalizes standard Bayesian to allow posterior reside on richer probability space than prior. then provide behavioral characterization this rule conclude that decision maker's subjective expected utility beliefs are consis...

Two ideas taken from Bayesian optimization and classifier systems are presented for personnel scheduling based on choosing a suitable scheduling rule from a set for each person’s assignment. Unlike our previous work of using genetic algorithms whose learning is implicit, the learning in both approaches is explicit, i.e. we are able to identify building blocks directly. To achieve this target, t...

User modeling is an iml>ortant COlnponents of dialog systems. Most previous approaches are rule-based methods, hi this paper, we proimse to represent user models th rough Bayesian networks. Some advantages of the Bayesian approach over the rule-based approach are as follows. First, rules for upda t ing user models are not necessary because up<lating is directly performed by the ewduation of the...

Integrating association rule discovery and classification in data mining brings a new approach known as associative classification. Associative classification is a promising approach that often constructs more accurate classification models (classifiers) than the traditional classification approaches such as decision trees and rule induction. In this research, the authors investigate the use of...

Two ideas taken from Bayesian optimization and classifier systems are presented for personnel scheduling based on choosing a suitable scheduling rule from a set for each person’s assignment. Unlike our previous work of using genetic algorithms whose learning is implicit, the learning in both approaches is explicit, i.e. we are able to identify building blocks directly. To achieve this target, t...

Genetic epidemiologists strive to determine the genetic profile of diseases. Epistasis is the interaction between two or more genes to affect phenotype. Due to the often non-linearity of the interaction, it is difficult to detect statistical patterns of epistasis. Combinatorial methods for detecting epistasis investigate a subset of combinations of genes without employing a search strategy. The...

What simple learning rules can allow agents to cope with changing environments? We tested whether a rule that neglects base rates of events in the world — something that is usually considered irrational — could be as successful as Bayesian inference — the usual standard of rationality — in making cuebased predictions about events in time-varying environments. We focused on environments in which...