Siddhartha Chib


Siddhartha Chib is a statistician and Professor of Econometrics and Statistics at Washington University in St. Louis. His work is primarily in Bayesian statistics, econometrics, and Markov chain Monte Carlo methods.
Key papers include Albert and Chib, which introduced an approach for binary and categorical response models based on latent variables that simplifies the Bayesian analysis of categorical response models; Chib and Greenberg, which provided a derivation of the Metropolis-Hastings algorithm from first principles, guidance on implementation and extensions to multiple-block versions; Kim, Shephard and Chib, which introduced an efficient inference approach for univariate and multivariate stochastic volatility models; Carlin and Chib, which developed a method for Bayesian model choice via Markov chain Monte Carlo methods; Chib and Chib and Jeliazkov, where a method for calculating the marginal likelihood from the Gibbs and Metropolis-Hastings output is developed, and Chib and Greenberg, on the Bayesian analysis of the multivariate probit model.
He has also written on Tobit censored responses hidden Markov processes, multiple change-points, discretely observed diffusions, univariate and multivariate ARMA processes, multivariate count responses, causal inference, hierarchical models of longitudinal data, and, in Chib, Shin and Simoni, on the Bayesian analysis of moment condition models.

Biography

He received a bachelor's degree from Delhi University in 1979. He earned a Ph.D. in Economics from the University of California, Santa Barbara in 1986. His advisor was Sreenivasa Rao Jammalamadaka.

Honors and awards

He is a fellow of the American Statistical Association, the International Society of Bayesian Analysis, and the Journal of Econometrics.

Selected publications