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• W2975653548 abstract "The Metropolis-Hastings method is often used to construct a Markov chain with a given $pi$ as its stationary distribution. The method works even if $pi$ is known only up to an intractable constant of proportionality. Polynomial time convergence results for such chains (rapid mixing) are hard to obtain for high dimensional probability models where the size of the state space potentially grows exponentially with the model dimension. In a Bayesian context, Yang, Wainwright, and Jordan (2016) (=YWJ) used the path method to prove rapid mixing for high dimensional linear models. This paper proposes a modification of the YWJ approach that simplifies the theoretical argument and improves the rate of convergence. The new approach is illustrated by an application to an exponentially weighted aggregation estimator." @default.
• W2975653548 created "2019-10-03" @default.
• W2975653548 creator A5042944255 @default.
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• W2975653548 date "2019-09-25" @default.
• W2975653548 modified "2023-10-17" @default.
• W2975653548 title "Rapid mixing of a Markov chain for an exponentially weighted aggregation estimator" @default.
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• W2975653548 doi "https://doi.org/10.48550/arxiv.1909.11773" @default.
• W2975653548 hasPublicationYear "2019" @default.
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