|Contact Details:||School of Computer Science and
Lloyd Institute (Room 105),
||+353 (0) 1 677 0711
Summary of Research:
The problem of performing online inference when observations arrive sequentially in time is well known in the statistical and engineering communities. Statisticians have come up with several state-of-the-art techniques to make inferences to online problems, while keeping a balance between accuracy and speed. My current research focuses on developing a new technique that would be able to cater for high dimensionality and speed at the same time.
I am trying to develop a functional approximation to prediction of general state space models, where the latent variable can be high dimensional and parameters can be assumed to be unknown. I plan to extend this is a spatial setting in the future.
General interests: I have a strong interest in Bayesian Statistics and its applications. I am also particulary interested in classical Time Series and regression, both linear and non-linear. State-Space modelling is my current area of interest.
Publications and Presentations:
B. Houlding, A. Battacharya, S. P. Wilson, and T. K. Forde, 'A fast Bayesian model for latent radio signal prediction'. Technical Report (09/02) , Trinity College Dublin, 2009.