Continuous-Time Models: Why and How

When:

Friday, June 28, 2013, 11:00 am - 12:00 pm

Where:

6th floor large conference room

Type:

AI Seminar

Speaker:

Christian Shelton, UC Riverside

Description:

Abstract:

 

Discrete-time models are abundant in artificial intelligence: hidden

Markov models, dynamic Bayesian networks, Markov decision processes,

and (most) auto-regressive models assume time passes in discrete jumps.

Yet, most processes modeled actually evolve in continuous time.  This talk

explores the problems inherent in this dichotomy, focusing on Markovian

models.

 

First, I will discuss the theoretic and experimental difficulties when

modeling in discrete time.  In doing so, I will present continuous-time

Markov processes, drawing analogies to their discrete-time counterparts.

Second, I will present the continuous-time analog of a dynamic Bayesian

network: a continuous-time Bayesian network (CTBN).  The talk will include

an overview of the learning and inference literatures for CTBNs, showing

how continuous-time aids in the development of efficient inference

techniques.  Finally, I will show some application results employing CTBNs

on real data sets.

 

Bio:

 

Christian R. Shelton is an Associate Professor of Computer Science at the

University of California at Riverside.  He has spent time as a visiting

researcher at Intel Research and Children's Hospital Los Angeles.  He was

the Managing Editor of the Journal of Machine Learning Research and on

the editorial board of the Editorial Board of the Journal of Artificial

Intelligence Research.

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