Coronavirus disease 2019 (COVID-19) has been termed a “Pandemic Disease” that infected many people and caused deaths on nearly unprecedented level. As more are each day, it continues to pose serious threat humanity worldwide. result, healthcare systems around the world facing shortage of medical space such as wards sickbeds. In most cases, healthy experience tolerable symptoms if they infected....

We develop a dynamic model of opinion formation in social networks when the information required for learning a payoff-relevant parameter may not be at the disposal of any single agent. Individuals engage in communication with their neighbors in order to learn from their experiences. However, instead of incorporating the views of their neighbors in a fully Bayesian manner, agents use a simple u...

A previous paper (2] showed how to generate a linear discriminant network (LDN) that computes likely faults for a noisy fault detection problem by using a modification of the perceptron learning algorithm called the pocket algorithm. Here we compare the performance of this connectionist model with performance of the optimal Bayesian decision rule for the example that was previously described. W...

Before we discuss the details of the Bayesian detection, let us take a quick tour about the overall framework to detect (or classify) an object in practice. In the Bayesian setting, we model observations as random samples drawn from some probability distributions. The classification process usually involves extracting features from the observations, and a decision rule that satisfies certain op...

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...