Siddhartha Chib

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Siddhartha Chib is an econometrician and 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 (1993) 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 (1995) which provided a derivation of the Metropolis-Hastings algorithm from first principles, guidance on implementation and extensions to multiple-block versions; Chib (1995) where a new method for calculating the marginal likelihood from the Gibbs output is developed; Chib and Jeliazkov (2001) where the method of Chib (1995) is extended to output of Metropolis-Hastings chains; Basu and Chib (2003) for a method for finding marginal likelihoods in Dirichlet process mixture models; Carlin and Chib (1995) which developed a model-space jump method for Bayesian model choice via Markov chain Monte Carlo methods; Chib (1998) which introduced a multiple-change point model that is estimated by the methods of Albert and Chib (1993) and Chib (1996) for hidden Markov processes; Kim, Shephard and Chib (1998) which introduced an efficient inference approach for univariate and multivariate stochastic volatility models; and Chib and Greenberg (1998) which developed the Bayesian analysis of the multivariate probit model. He has also developed original methods for Bayesian inference in Tobit censored responses, discretely observed diffusions, univariate and multivariate ARMA processes, multivariate count responses, causal inference, hierarchical models of longitudinal data, and, in Chib, Shin and Simoni (2018, 2020), methods for the Bayesian analysis of unconditional and conditional moment condition models. (Source: DBPedia)