Approximation by quantization of the filter process and applications to optimal stopping problems under partial observation

Computational methods in Markov chains (60J22) Probabilistic models, generic numerical methods in probability and statistics (65C20) Markov processes: estimation; hidden Markov models (62M05) Signal detection and filtering (aspects of stochastic processes) (60G35) Markov chains (discrete-time Markov processes on discrete state spaces) (60J10) Applications of Markov chains and discrete-time Markov processes on general state spaces (social mobility, learning theory, industrial processes, etc.) (60J20) Stopping times; optimal stopping problems; gambling theory (60G40)

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An Introduction to Particle Methods with Financial Applications ⋮ American Option Valuation with Particle Filters ⋮ Optimal Delaunay and Voronoi Quantization Schemes for Pricing American Style Options ⋮ Partially observed optimal stopping problem for discrete-time Markov processes ⋮ Asymptotics of the maximal radius of an \(L^{r}\)-optimal sequence of quantizers ⋮ Learning the random variables in Monte Carlo simulations with stochastic gradient descent: Machine learning for parametric PDEs and financial derivative pricing ⋮ Change-point detection for piecewise deterministic Markov processes ⋮ Optimal stopping for partially observed piecewise-deterministic Markov processes ⋮ Recursive Marginal Quantization of the Euler Scheme of a Diffusion Process ⋮ Greedy vector quantization ⋮ Convergence of Markovian Stochastic Approximation with Discontinuous Dynamics ⋮ Pointwise Convergence of the Lloyd I Algorithm in Higher Dimension ⋮ A simulation approach to optimal stopping under partial information

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