About the Project Consider a large number of detectors each generating a data stream. The task is to detect online, distribution changes in a small fraction of the data streams. We propose optimal algorithms that minimize the detection delay subject to a given average run length constraint. We also…

About the Project Variational inference methods are very useful in the analysis of large datasets. The key idea of such methods for Bayesian inference is to reformulate the problem of approximating a posterior distribution as an optimization problem. Recent progress in the area has been concerned with the application…

About the Project Approximate Bayesian computation (ABC) is a paradigm which allows one to perform Bayesian statistical inference, when the associate probability is totally intractable in a certain manner. In order to conduct this afore-mentioned inference, one must resort to Monte Carlo estimation. A/P Jasra and Dr Jo are…

About the Project Mean field game is an approximation for stochastic games with many players.The results that they obtained will be applied to the investigation of systemic risk in limit order books and global energy markets, both of which have large number of players. The analytical tools used in…

About the Project Convex composite conic optimization problems have found widespread applications in a wide variety of domains such as operations research (e.g. relaxation of combinatorial and polynomial optimization problems), machine learning (e.g. classification, clustering, completion), statistics (e.g. regression, covariance estimation, graphical model, compressed sensing), and engineering (e.g. optimal…