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Books on the topic 'Markov approximation'

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Published: 14 March 2023

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1

Markov operators, positive semigroups, and approximation processes. Berlin: Walter de Gruyter GmbH & Co., KG, 2015.

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2

Bayer, Valentina. Approximation algorithms for solving cost observable Markov decision processes. Corvallis, OR: Oregon State University, Dept. of Computer Science, 1999.

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3

Komorowski, Tomasz. Fluctuations in Markov Processes: Time Symmetry and Martingale Approximation. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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4

American-type options: Stochastic approximation methods. Berlin: Walter de Gruyter GmbH & Co. KG, 2014.

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5

Zheng, Shuheng. Stochastic Approximation Algorithms in the Estimation of Quasi-Stationary Distribution of Finite and General State Space Markov Chains. [New York, N.Y.?]: [publisher not identified], 2014.

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6

Aldous, D. J. Probability approximations via the Poisson clumping heuristic. New York: Springer-Verlag, 1989.

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7

Chang-Jin, Kim. In search of a model that an ARCH-type model may be approximating: The Markov model of heteroskedasticity. [Toronto, Ont: York University, Dept. of Economics, 1990.

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8

Koli︠a︡da, S. F. Dynamics and numbers: A special program, June 1-July 31, 2014, Max Planck Institute for Mathematics, Bonn, Germany : international conference, July 21-25, 2014, Max Planck Institute for Mathematics, Bonn, Germany. Edited by Max-Planck-Institut für Mathematik. Providence, Rhode Island: American Mathematical Society, 2016.

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9

Altomare, Francesco, Mirella Cappelletti, Ioan Rasa, and Vita Leonessa. Markov Operators, Positive Semigroups and Approximation Processes. de Gruyter GmbH, Walter, 2015.

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10

Altomare, Francesco, Mirella Cappelletti, Ioan Rasa, and Vita Leonessa. Markov Operators, Positive Semigroups and Approximation Processes. de Gruyter GmbH, Walter, 2014.

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11

Landim, Claudio, Tomasz Komorowski, and Stefano Olla. Fluctuations in Markov Processes: Time Symmetry and Martingale Approximation. Springer, 2014.

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12

Chabanyuk, Yaroslav, Anatolii Nikitin, and Uliana Khimka. Asymptotic Analyses for Complex Evolutionary Systems with Markov and Semi-Markov Switching Using Approximation Schemes. Wiley & Sons, Incorporated, John, 2020.

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13

Chabanyuk, Yaroslav, Anatolii Nikitin, and Uliana Khimka. Asymptotic Analyses for Complex Evolutionary Systems with Markov and Semi-Markov Switching Using Approximation Schemes. Wiley & Sons, Incorporated, John, 2020.

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14

Chabanyuk, Yaroslav, Anatolii Nikitin, and Uliana Khimka. Asymptotic Analyses for Complex Evolutionary Systems with Markov and Semi-Markov Switching Using Approximation Schemes. Wiley & Sons, Incorporated, John, 2020.

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15

Chabanyuk, Yaroslav, Anatolii Nikitin, and Uliana Khimka. Asymptotic Analyses for Complex Evolutionary Systems with Markov and Semi-Markov Switching Using Approximation Schemes. Wiley & Sons, Incorporated, John, 2020.

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16

Bubley, Russ. Randomized Algorithms: Approximation, Generation, and Counting. Springer London, Limited, 2011.

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17

Bubley, Russ. Randomized Algorithms: Approximation, Generation and Counting (Distinguished Dissertations). Springer, 2000.

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18

Alfonsi, Aurélien. Affine Diffusions and Related Processes: Simulation, Theory and Applications. Springer, 2016.

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19

Alfonsi, Aurélien. Affine Diffusions and Related Processes: Simulation, Theory and Applications. Springer International Publishing AG, 2015.

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20

Alfonsi, Aurélien. Affine Diffusions and Related Processes: Simulation, Theory and Applications. Springer, 2015.

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21

Functional Gaussian Approximation For Dependent Structures. Oxford, UK: Oxford University Press, 2019.

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22

Stoyan, Dietrich. Qualitative Eigenschaften und Abschätzungen Stochastischer Modelle. de Gruyter GmbH, Walter, 2022.

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23

Fuchs, Christiane. Inference for Diffusion Processes: With Applications in Life Sciences. Springer, 2015.

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24

Freedman, David. Approximating Countable Markov Chains. Springer, 2011.

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25

Freedman, David. Approximating Countable Markov Chains. Springer London, Limited, 2012.

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26

Fuchs, Christiane. Inference for Diffusion Processes: With Applications in Life Sciences. Springer London, Limited, 2013.

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27

Fuchs, Christiane. Inference for Diffusion Processes: With Applications in Life Sciences. Springer, 2013.

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28

Fuchs, Christiane. Inference for Diffusion Processes: With Applications in Life Sciences. Springer, 2013.

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29

Stein's method: Expository lectures and applications. Beachwood, Ohio: Institute of Mathematical Statistics, 2004.

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30

(Editor), Persi Diaconis, and Susan Holmes (Editor), eds. Stein's Method: Expository Lectures and Applications (Institute of Mathematical Statistics, Lecture Notes-Monograph Series). Institute of Mathematical Statistics, 2004.

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31

Almudevar, Anthony Louis. Approximate Iterative Algorithms. Taylor & Francis Group, 2014.

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32

Approximate Iterative Algorithms. CRC Press, 2014.

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33

Almudevar, Anthony Louis. Approximate Iterative Algorithms. Taylor & Francis Group, 2014.

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34

Almudevar, Anthony Louis. Approximate Iterative Algorithms. Taylor & Francis Group, 2014.

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35

Almudevar, Anthony Louis. Approximate Iterative Algorithms. Taylor & Francis Group, 2014.

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36

Almudevar, Anthony Louis. Approximate Iterative Algorithms. Taylor & Francis Group, 2014.

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37

Almudevar, Anthony Louis. Approximate Iterative Algorithms. Taylor & Francis Group, 2014.

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38

Phase-Type Approximations for Wear Processes in A Semi-Markov Environment. Storming Media, 2004.

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39

Aldous, David. Probability Approximations Via the Poisson Clumping Heuristic. Springer London, Limited, 2013.

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40

Aldous, David. Probability Approximations via the Poisson Clumping Heuristic. Springer New York, 2010.

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41

Renshaw, Eric. Stochastic Population Processes: Analysis, Approximations, Simulations. Oxford University Press, 2015.

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42

Stochastic Population Processes: Analysis, Approximations, Simulations. Oxford University Press, 2011.

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43

Schliesser, Eric. The Methodology of Wealth of Nations. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190690120.003.0012.

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This chapter aims to give an interpretation of Adam Smith’s main methods in Wealth of Nations. It does so by focusing on the crucial analytical distinction between natural prices and market prices. Smith postulates a “natural course” of events in order to stimulate research into institutions and other causes that cause actual events to deviate from it. For Smith, scientific theory is among other functions, a research tool that allows for a potentially open-ended process of successive approximation. Smith’s explanation of the introduction of commerce in Europe is contrasted with that of his friend and forerunner David Hume. Hume’s essay “Of the Populousness of Ancient Nations” may have inspired Smith to develop his method.
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44

Boudreau, Joseph F., and Eric S. Swanson. Interpolation and extrapolation. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198708636.003.0004.

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This chapter deals with two related problems occurring frequently in the physical sciences: first, the problem of estimating the value of a function from a limited number of data points; and second, the problem of calculating its value from a series approximation. Numerical methods for interpolating and extrapolating data are presented. The famous Lagrange interpolating polynomial is introduced and applied to one-dimensional and multidimensional problems. Cubic spline interpolation is introduced and an implementation in terms of Eigen classes is given. Several techniques for improving the convergence of Taylor series are discussed, including Shank’s transformation, Richardson extrapolation, and the use of Padé approximants. Conversion between representations with the quotient-difference algorithm is discussed. The exercises explore public transportation, human vision, the wine market, and SU(2) lattice gauge theory, among other topics.
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45

Cheng, Russell. Finite Mixture Models. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198505044.003.0017.

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Fitting a finite mixture model when the number of components, k, is unknown can be carried out using the maximum likelihood (ML) method though it is non-standard. Two well-known Bayesian Markov chain Monte Carlo (MCMC) methods are reviewed and compared with ML: the reversible jump method and one using an approximating Dirichlet process. Another Bayesian method, to be called MAPIS, is examined that first obtains point estimates for the component parameters by the maximum a posteriori method for different k and then estimates posterior distributions, including that for k, using importance sampling. MAPIS is compared with ML and the MCMC methods. The MCMC methods produce multimodal posterior parameter distributions in overfitted models. This results in the posterior distribution of k being biased towards high k. It is shown that MAPIS does not suffer from this problem. A simple numerical example is discussed.
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46

Castro, Guillermo. El negocio jurídico, la declaración de voluntad y el ordenamiento jurídico. Editorial Universidad Católica de Colombia, 2020. http://dx.doi.org/10.14718/9789585133310.2020.

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Uwe Wesel, professor of law at the Free University of Berlin, expressed in several of his writings an interesting thought: more than the law of obligations (Schuldrecht) the law of contracts (Vertragsrecht) is a better metadiscourse to reread and interpret the spectrum of The legal, which could even give an accurate account of the history of Western law. From the origins of the Greek civilization and to the Lisbon agreement, the contract was the institute that most connected the legal with the reasonableness, fairness, proportionality, justice and the conservative and protective sense of the community; of course also with the market economy and globalization. For this reason, this diffuse approximation of the contract and the legal business within western juridicity provides interesting inputs, such as inadvertent responses to how certain constitutional principles emerged? Or why do freedom and equality rights always entail obligations? under many others perspectives.
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