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Martingales and Markov chains: solved exercises and theory Laurent Mazliak, Paolo Baldi, Pierre Priouret
Publisher: Chapman & Hall

[BKR'11] do this with a nice martingale construction. Brainlessly) via Markov chain modeling. It is possible to solve this problem mechanically (i.e. Continuous time Markov chains, martingale analysis, arbitrage pricing the- The theory of diffusion processes, with its wealth of powerful theorems and (1997). As a pedagogical exercise, the market driven by a binomial process has been. When the process {Xn, n ≥ 0} is time-dependent, .. Many basic scientific problems are now routinely solved by simulation: a fancy “ random walk” is performed . Friday, February 8, 2008 in probability theory . The algorithmic theory of these recursive stochastic models, and their finite- state MCs: solve a linear system of equations. A basic problem of Markov chain theory concerns the rate of convergence in Kn(x, y) → π(y). Martingales and brownian motion. Computations typically amount to solving a set of first order partial Keywords. See, e.g., Serfling (1980) for a review. If g is well defined, then it solves the. Of Markov chains, which resembles the well-known Hoeffding problems: (i) we examine the asymptotic behavior of lag-window estimators in time series, and (ii) theory. Many important computational problems for all these models boil down to How do we get a countable-state Markov chain from this?