We study the impact of observational learning in large scale congested service systems with servers having heterogenous …, identifying Bayesian equilibria is intractable with a large, discrete number of servers. In this paper, we develop a tractable … model with a continuum of servers. We find that the impact of observational learning on the customers' choice behavior may …

. The Pearl algorithm in Bayesian belief networks induces a belief network from data. With a solid grounding in probability … theory, the Pearl algorithm allows belief updating by propagating likelihoods of leaf nodes (variables) and the prior … this model were then compared to a Linear Regression model. The Bayesian belief network outperformed stepwise linear …

though the neighbors’ views may be quite inaccurate). This non-Bayesian learning rule is motivated by the formidable …We develop a dynamic model of opinion formation in social networks when the information required for learning a payoff … learn from their experiences. However, instead of incorporating the views of their neighbors in a fully Bayesian manner …

while learning the distribution of a dose-response parameter from a cohort of patients. We provide a Bayesian stochastic …, approximate solution of the optimal learning problem. Computer simulations using the Michaelis-Menten dose-response function are … included as an example wherein we study the effect of cohort size and prior misspecification on learning, and also compare the …