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\centerline{\bf STANFORD UNIVERSITY}
\centerline{\bf DEPARTMENT OF STATISTICS}
\centerline{\big DEPARTMENTAL SEMINAR}
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\centerline{4:15 p.m., Tuesday, April 30, 2002}
\centerline{Sequoia Hall Room 200}
\centerline{(Cookies at 3:45 in 1st Floor Lounge)}

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\centerline{\sl Tze Leung Lai}
\centerline{\sl Department of Statistics}
\centerline{\sl Stanford University}
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\centerline{\bf Filtering, Smoothing and Parameter Estimation in Hidden Markov Models}
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  After a brief overview of hidden Markov models and their applications
in engineering, economics and bioinformatics, we consider a number of
long-standing problems concerning efficient parameter estimation and
optimal filters and smoothers when the underlying Markov chain takes
values in a general (possibly infinite and multidimensional) state space.
We describe certain bounded-complexity approximations and simulation-based
methods that are asymptotically as efficient as the computationally
intractable maximum likelihood estimators and Bayesian filters and
smoothers.

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