4 accepted papers
We propose a Bayesian decision making framework for control of Markov Decision Processes (MDPs) with unknown dynamics and large, possibly continuous, state, action, and parameter spaces in data-poor environments. Most of the existing adaptive controllers for MDPs with unknown dynamics are based on t…
We extend the popular Top Scoring Pair (TSP) classification rule to a Bayesian setting, with the purpose of obtaining robust and effective classifiers for small-sample, high-dimensional data. We employ the Bradley-Terry model for rank data, and infer its parameters using a previously proposed Gibbs…
This paper is concerned with optimal estimation of the state of a Boolean dynamical systems observed through correlated noisy Boolean measurements. The optimal Minimum Mean-Square Error (MMSE) state estimator for general Partially-Observed Boolean Dynamical Systems (POBDS) can be computed via the Bo…
This paper studies the classification of gene regulatory networks (GRNs) modeled by probabilistic Boolean networks (PBNs). After observing Gaussian expression values of n genes at m consecutive time points, with consideration of missing data, an algorithm based on expectation maximization (EM) is pr…