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pages 527-568
Nonparametric Bayesian Inference for Stochastic Processes with Piecewise Constant Priors
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Publication type: Book Chapter
Publication date: 2024-06-01
Abstract
We present a survey of some of our recent results on Bayesian nonparametric inference for a multitude of stochastic processes. The common feature is that the prior distribution in the cases considered is on suitable sets of piecewise constant or piecewise linear functions, that differ for the specific situations at hand. Posterior consistency and in most cases contraction rates for the estimators are presented. Numerical studies on simulated and real data accompany the theoretical results.
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BELOMESTNY D. et al. Nonparametric Bayesian Inference for Stochastic Processes with Piecewise Constant Priors // MATRIX Book Series. 2024. pp. 527-568.
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BELOMESTNY D., Van Der Meulen F., SPREIJ P. Nonparametric Bayesian Inference for Stochastic Processes with Piecewise Constant Priors // MATRIX Book Series. 2024. pp. 527-568.
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TY - GENERIC
DO - 10.1007/978-3-031-47417-0_28
UR - https://link.springer.com/10.1007/978-3-031-47417-0_28
TI - Nonparametric Bayesian Inference for Stochastic Processes with Piecewise Constant Priors
T2 - MATRIX Book Series
AU - BELOMESTNY, DENIS
AU - Van Der Meulen, Frank
AU - SPREIJ, PETER
PY - 2024
DA - 2024/06/01
PB - Springer Nature
SP - 527-568
SN - 2523-3041
SN - 2523-305X
ER -
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@incollection{2024_BELOMESTNY,
author = {DENIS BELOMESTNY and Frank Van Der Meulen and PETER SPREIJ},
title = {Nonparametric Bayesian Inference for Stochastic Processes with Piecewise Constant Priors},
publisher = {Springer Nature},
year = {2024},
pages = {527--568},
month = {jun}
}