About Research
Currently the leading team in the world in designing algorithms for solving large scale convex Optimisation problems, Developing other tools in Financial engineering, Sequential MC, Robust Optimization and learning algorithms. The Analytics group has strong interest in solving large scale optimization problems, with applications to data analytics.
Principal Investigators
Recent Projects
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 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 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 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 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…
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…
Recent Publications
Hock Peng Chan
Consider a large number of detectors each generating a datastream. The task is to detect online, distribution changes in a smallfraction of the data streams. Previous approaches to this probleminclude the use of mixture likelihood ratios and sum of CUSUMs. Weprovide here extensions and modifications of these approaches thatare…
Suported by the National University of Singapore grant R-155-000-158-112
Hock Peng Chan
Consider a large number of detectors each generating a datastream. The task is to detect online, distribution changes in a smallfraction of the data streams. Previous approaches to this probleminclude the use of mixture likelihood ratios and sum of CUSUMs. Weprovide here extensions and modifications of these approaches thatare…
The Annals of Statistics
Suported by the National University of Singapore grant R-155-000-158-112
Ajay Jasra, Seongil Jo, David Nott, Christine Shoemaker, Raul Tempone
In the following article we consider approximate Bayesian computation (ABC) inference. We introduce a method for numerically approximating ABC posteriors using the multilevel Monte Carlo (MLMC). A sequential Monte Carlo version of the approach is developed and it is shown under some assumptions that for a given level of…
Ajay Jasra, Seongil Jo, David Nott, Christine Shoemaker, Raul Tempone
In the following article we consider approximate Bayesian computation (ABC) inference. We introduce a method for numerically approximating ABC posteriors using the multilevel Monte Carlo (MLMC). A sequential Monte Carlo version of the approach is developed and it is shown under some assumptions that for a given level of…
Stochastic Analysis and Applications
