In this contribution we describe an object{oriented software architecture for image segmentation, 3{D pose estimation as well as Bayesian object recognition: models are represented by densities, model generation corresponds to parameter estimation tasks, and the identi cation applies the Bayesian decision rule. We show results of 3{D object recognition experiments based on the observation of 2{...

This paper proposes a method to construct an adaptive agent that is universal with respect to a given class of experts, where each expert is designed specifically for a particular environment. This adaptive control problem is formalized as the problem of minimizing the relative entropy of the adaptive agent from the expert that is most suitable for the unknown environment. If the agent is a pas...

Bayesian models of cognition hypothesize that human brains make sense of data by representing probability distributions and applying Bayes’ rule to find the best explanation for available data. Understanding the neural mechanisms underlying probabilistic models remains important because Bayesian models provide a computational framework, rather than specifying mechanistic processes. Here, we pro...

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...

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...