other · 2020
Stochastic Approximation Procedure
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Abstract
This chapter discusses jumping stochastic approximation procedure in a Markov environment under the averaging scheme. It illustrates the conditional expectation of a two-component Markov process. The chapter describes the jumping stochastic approximation procedure under diffusion approximating scheme and shows that the investigation of the stochastic approximation procedure in a random Markov environment requires a statistical analysis of the Markov environment. It investigates the convergence of jumping stochastic approximation procedure in a semi-Markov environment using the asymptotic properties of the compensating operator for the extended Markov renewal process and solving its singular perturbation problem. The chapter also considers the asymptotic normality of a continuous procedure of stochastic approximation in the case where the regression function contains a singularly perturbed term depending on the external medium described by a uniformly ergodic Markov process.
Cite this paper
(2020). Stochastic Approximation Procedure. https://doi.org/10.1002/9781119779759.ch4
“Stochastic Approximation Procedure.” 2020, pp. 73–133, https://doi.org/10.1002/9781119779759.ch4.
2020. “Stochastic Approximation Procedure.” https://doi.org/10.1002/9781119779759.ch4.
(2020) ‘Stochastic Approximation Procedure’. Available at: https://doi.org/10.1002/9781119779759.ch4.
@misc{anon2020stochastic,
title = {{Stochastic Approximation Procedure}},
pages = {73--133},
year = {2020},
doi = {10.1002/9781119779759.ch4}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.