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Published: 4 June 2021
Last updated: 28 January 2023

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1

Banisch, Sven. Markov Chain Aggregation for Agent-Based Models. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-24877-6.

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2

Ching, Wai-Ki. Markov Chains: Models, Algorithms and Applications. 2nd ed. Boston, MA: Springer US, 2013.

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3

Meyer, Carl D., and Robert J. Plemmons, eds. Linear Algebra, Markov Chains, and Queueing Models. New York, NY: Springer New York, 1993. http://dx.doi.org/10.1007/978-1-4613-8351-2.

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4

Yücesan, Enver. Analysis of Markov chains using simulation graph models. Fontainebleau: INSEAD, 1990.

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5

Bioinformatics: Sequence alignment and Markov models. New York: McGraw-Hill, 2009.

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6

Saad, Y. Projection methods for the numerical solution of Markov chain models. [Moffett Field, Calif.]: Research Institute for Advanced Computer Science, NASA Ames Research Center, 1989.

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7

Penny, D. Modeling the covarion model of molecular evolution by hidden Markov chains. Palmerston North, N.Z: Massey University College of Sciences, 1998.

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8

Mo, Jeonghoon. Performance modeling of communication networks with Markov chains. San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA): Morgan & Claypool, 2010.

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9

N, Limnios, ed. Semi-Markov chains and hidden semi-Markov models toward applications: Their use in reliability and DNA analysis. New York: Springer, 2008.

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10

Bagnoli, Carlo, Alessia Bravin, Maurizio Massaro, and Alessandra Vignotto. Business Model 4.0. Venice: Edizioni Ca' Foscari, 2018. http://dx.doi.org/10.30687/978-88-6969-286-4.

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The manufacturing digital transformation is changing the industry through the introduction of advanced solutions that allow companies to re-interpret their role along the value chain. The industrial revolution opens up great opportunities for Italian companies, in terms of process efficiency, cost reduction and improvement in productivity, but also in the rethinking of products, new services, and the ability of reaction to market needs. This report examines the possible impact of Industry 4.0 on business models considering technological innovation also as a driver of strategic innovation.
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11

Groen, Maria Margaretha de. Modelling interception and transpiration at monthly time steps: Introducing daily variability through Markov chains. Lisse: Swets & Zeitlinger, 2002.

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12

author, Sarich Marco 1985, ed. Metastability and Markov state models in molecular dynamics: Modeling, analysis, algorithmic approaches. Providence, Rhode Island: American Mathematical Society, 2013.

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13

Lim, Kian Guan. Probability and finance theory. New Jersey: World Scientific, 2011.

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14

Probability and finance theory. Hackensack, NJ: World Scientific, 2015.

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15

Sharma, Kal Renganathan. Bioinformatics. New York: McGraw-Hill, 2008.

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16

O, Seppäläinen Timo, ed. A course on large deviations with an introduction to Gibbs measures. Providence, Rhode Island: American Mathematical Society, 2015.

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17

Sun, Shuying. Haplotype inference using a hidden Markov model with efficient Markov chain sampling. 2007, 2007.

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18

Henderson, Daniel A., R. J. Boys, Carole J. Proctor, and Darren J. Wilkinson. Linking systems biology models to data: A stochastic kinetic model of p53 oscillations. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.7.

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This article discusses the use of a stochastic kinetic model to study protein level oscillations in single living cancer cells, using the p53 and Mdm2 proteins as examples. It describes the refinement of a dynamic stochastic process model of the cellular response to DNA damage and compares this model to time course data on the levels of p53 and Mdm2. The article first provides a biological background on p53 and Mdm2 before explaining how the stochastic kinetic model is constructed. It then introduces the stochastic kinetic model and links it to the data and goes on to apply sophisticated MCMC methods to compute posterior distributions. The results demonstrate that it is possible to develop computationally intensive Markov chain Monte Carlo (MCMC) methods for conducting a Bayesian analysis of an intra-cellular stochastic systems biology model using single-cell time course data.
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19

Keilson, J. Markov Chain Models -- Rarity and Exponentiality. Springer London, Limited, 2012.

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20

Quintana, José Mario, Carlos Carvalho, James Scott, and Thomas Costigliola. Extracting S&P500 and NASDAQ Volatility: The Credit Crisis of 2007–2008. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.13.

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This article demonstrates the utility of Bayesian modelling and inference in financial market volatility analysis, using the 2007-2008 credit crisis as a case study. It first describes the applied problem and goal of the Bayesian analysis before introducing the sequential estimation models. It then discusses the simulation-based methodology for inference, including Markov chain Monte Carlo (MCMC) and particle filtering methods for filtering and parameter learning. In the study, Bayesian sequential model choice techniques are used to estimate volatility and volatility dynamics for daily data for the year 2007 for three market indices: the Standard and Poor’s S&P500, the NASDAQ NDX100 and the financial equity index called XLF. Three models of financial time series are estimated: a model with stochastic volatility, a model with stochastic volatility that also incorporates jumps in volatility, and a Garch model.
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21

van Moerbeke, Pierre. Determinantal point processes. Edited by Gernot Akemann, Jinho Baik, and Philippe Di Francesco. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198744191.013.11.

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This article presents a list of algebraic, combinatorial, and analytic mechanisms that give rise to determinantal point processes. Determinantal point processes have been used in random matrix theory (RMT) since the early 1960s. As a separate class, determinantal processes were first used to model fermions in thermal equilibrium and the term ‘fermion’ point processes were adopted. The article first provides an overview of the generalities associated with determinantal point processes before discussing loop-free Markov chains, that is, the trajectories of the Markov chain do not pass through the same point twice almost surely. It then considers the measures given by products of determinants, namely, biorthogonal ensembles. An especially important subclass of biorthogonal ensembles consists of orthogonal polynomial ensembles. The article also describes L-ensembles, a general construction of determinantal point processes via the Fock space formalism, dimer models, uniform spanning trees, Hermitian correlation kernels, and Pfaffian point processes.
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22

Banisch, Sven. Markov Chain Aggregation for Agent-Based Models. Springer London, Limited, 2015.

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23

Banisch, Sven. Markov Chain Aggregation for Agent-Based Models. Springer International Publishing AG, 2018.

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24

Bielelcki, Tomasz R., Stéphane Crépey, and Alexander Herbertsson. Markov Chain Models of Portfolio Credit Risk. Oxford University Press, 2011. http://dx.doi.org/10.1093/oxfordhb/9780199546787.013.0010.

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25

Banisch, Sven. Markov Chain Aggregation for Agent-Based Models. Springer, 2016.

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26

Ng, Michael K., Wai-Ki Ching, Ximin Huang, and Tak-Kuen Siu. Markov Chains: Models, Algorithms and Applications. Springer, 2013.

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27

Ng, Michael K., and Wai-Ki Ching. Markov Chains: Models, Algorithms and Applications. Springer, 2010.

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28

Markov Chains: Models, Algorithms and Applications. Boston: Kluwer Academic Publishers, 2006. http://dx.doi.org/10.1007/0-387-29337-x.

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29

Ng, Michael K., and Wai-Ki Ching. Markov Chains: Models, Algorithms and Applications. Springer, 2006.

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30

Ng, Michael K., Wai-Ki Ching, Ximin Huang, and Tak-Kuen Siu. Markov Chains: Models, Algorithms and Applications. Springer, 2015.

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31

Semi-Markov Chains and Hidden Semi-Markov Models toward Applications. New York, NY: Springer New York, 2008. http://dx.doi.org/10.1007/978-0-387-73173-5.

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32

D, Meyer C., and Plemmons Robert J, eds. Linear algebra, Markov chains, and queueing models. New York: Springer-Verlag, 1993.

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33

Carl D. Meyer Robert J. Plemmons. Linear Algebra, Markov Chains, and Queueing Models. Springer, 2011.

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34

Meyer, Carl D., and Robert J. Plemmons. Linear Algebra, Markov Chains, and Queueing Models. Springer, 2012.

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35

Bao, Yun, Carl Chiarella, and Boda Kang. Particle Filters for Markov-Switching Stochastic Volatility Models. Edited by Shu-Heng Chen, Mak Kaboudan, and Ye-Rong Du. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199844371.013.9.

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This chapter proposes an auxiliary particle filter algorithm for inference in regime switching stochastic volatility models in which the regime state is governed by a first-order Markov chain. It proposes an ongoing updated Dirichlet distribution to estimate the transition probabilities of the Markov chain in the auxiliary particle filter. A simulation-based algorithm is presented for the method that demonstrates the ability to estimate a class of models in which the probability that the system state transits from one regime to a different regime is relatively high. The methodology is implemented in order to analyze a real-time series, namely, the foreign exchange rate between the Australian dollar and the South Korean won.
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36

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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37

Mo, Jeonghoon. Performance Modeling of Communication Networks with Markov Chains. Springer International Publishing AG, 2010.

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38

Laver, Michael, and Ernest Sergenti. Systematically Interrogating Agent-Based Models. Princeton University Press, 2017. http://dx.doi.org/10.23943/princeton/9780691139036.003.0004.

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This chapter develops the methods for designing, executing, and analyzing large suites of computer simulations that generate stable and replicable results. It starts with a discussion of the different methods of experimental design, such as grid sweeping and Monte Carlo parameterization. Next, it demonstrates how to calculate mean estimates of output variables of interest. It does so by first discussing stochastic processes, Markov Chain representations, and model burn-in. It focuses on three stochastic process representations: nonergodic deterministic processes that converge on a single state; nondeterministic stochastic processes for which a time average provides a representative estimate of the output variables; and nondeterministic stochastic processes for which a time average does not provide a representative estimate of the output variables. The estimation strategy employed depends on which stochastic process the simulation follows. Lastly, the chapter presents a set of diagnostic checks used to establish an appropriate sample size for the estimation of the means.
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39

Limnios, Nikolaos, and Vlad Stefan Barbu. Semi-Markov Chains and Hidden Semi-Markov Models Toward Applications: Their Use in Reliability and DNA Analysis. Springer, 2009.

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40

Markov Chains: Models, Algorithms and Applications (International Series in Operations Research & Management Science). Springer, 2005.

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41

Boudreau, Joseph F., and Eric S. Swanson. Quantum field theory. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198708636.003.0024.

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Markov chain Monte Carlo techniques are developed to compute properties of a variety of quantum field theories. The method is introduced with a simple scalar field theory and used to evaluate the particle spectrum and phase diagram for parity symmetry breaking. The technique of micorcanonical updating is introduced to increase efficiency. The important topic of gauge theory is then introduced via the gauged Z2 model. Development of the gauge theory formalism continues with Abelian gauge theory in two dimensions. The interaction between static charges is computed and compared to the exact result. The string tension in nonableian SU(2) gauge theory is explored with the aid of the renormalization group, which gives an entrée to a discussion of the Higgs mechanism. Finally, the formalism for including fermions is briefly reviewed.
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42

SINGH, Dr ANIMESH, Dr BHAWNA CHOUDHARY, and Dr MANISHA GUPTA. TRANSFORMING BUSINESS THROUGH DIGITALIZATION. KAAV PUBLICATIONS, DELHI, INDIA, 2021. http://dx.doi.org/10.52458/9789391842390.2021.eb.

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The theme of this book “Transforming Business through Digitization‖ was chosen due to its relevance in the contemporary globalized world. The world is witnessing the pace of change of digitalization like never before the similar trend will be seen in future too. With integration of value chains and supply chains becoming a global imperative, the contribution of IT enabled services and digitalization has had great impact on Tran‘s nationalisation of businesses. The responsiveness in the value chains and in the larger supply chains will be the key to increasing the market share in future. The application of Artificial Intelligence has helped the stakeholders in value chains and supply chains in making informed & quick decisions. This has been made possible due to integrated and well organized businesses linkages leading to better storage, access and management of data. The increase digitalization and ability to track and capture data at different nodes in the value chain and supply chain will help the marketers understand the impact of various variables on the sales performance of various brands. The marketers have to work of ways to convince the stakeholders about the privacy of the data. In future there is a possibility of mixing compete data privacy with fluid artificial intelligence across the supply chain making business processes easier using the technology of block chains. The most important contribution of the digitalization in the supply chain may be seen in the area of sustainability and green initiatives. The may be made possible by the way of assessing the levels of reduction in exploitative and polluting systems and processes and making progressive modifications in those systems and processes. The book- ―transforming business through digitization‖ is an attempt to record Innovative and novel manuscripts, research-based articles, case studies, conceptual outcome-oriented business models, and practices from the innovative minds of researchers and academicians. The book encompasses twenty-four chapters with research-based perspectives in the area of e-commerce, digital governance, digital transaction platforms, business analytics, and digitalization in agriculture, digital marketing, block chain, nuero marketing, search engine marketing, UPIs, Search Engine Marketing, Digi-preneurship, and digital finance. The book can be read as a compendium of readings of digitization of business and industry.
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43

Rubin, Donald, Xiaoqin Wang, Li Yin, and Elizabeth Zell. Bayesian causal inference: Approaches to estimating the effect of treating hospital type on cancer survival in Sweden using principal stratification. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.24.

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This article discusses the use of Bayesian causal inference, and more specifically the posterior predictive approach of Rubin’s causal model (RCM) and methods of principal stratification, in estimating the effects of ‘treating hospital type’ on cancer survival. Using the Karolinska Institute in Stockholm, Sweden, as a case study, the article investigates which type of hospital (large patient volume vs. small volume) is superior for treating certain serious conditions. The study examines which factors may reasonably be considered ignorable in the context of covariates available, as well as non-compliance complications due to transfers between hospital types for treatment. The article first provides an overview of the general Bayesian approach to causal inference, primarily with ignorable treatment assignment, before introducing the proposed approach and motivating it using simple method-of-moments summary statistics. Finally, the results of simulation using Markov chain Monte Carlo (MCMC) methods are presented.
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44

Delsol, Laurent. Nonparametric Methods for α-Mixing Functional Random Variables. Edited by Frédéric Ferraty and Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.5.

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This article considers how functional kernel methods can be used to study α-mixing datasets. It first provides an overview of how prediction problems involving dependent functional datasets may arise from the study of time series, focusing on the standard discretized model and modelization that takes into account the functional nature of the evolution of the quantity to be studied over time. It then considers strong mixing conditions, with emphasis on the notion of α-mixing coefficients and α-mixing variables introduced by Rosenblatt (1956). It also describes some conditions for a Markov chain to be α-mixing; some useful tools that provide covariance inequalities, exponential inequalities, and Central Limit Theorem (CLT) for α-mixing sequences; the asymptotic properties of functional kernel estimators; the use of kernel smoothing methods with α-mixing datasets; and various functional kernel estimators corresponding to different prediction methods. Finally, the article highlights some interesting prospects for further research.
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45

Mo, Jeonghoon. Performance Modeling of Communication Networks with Markov Chains: Robustness, Uncertainty Quantification, and Insights Towards Safety. Springer International Publishing AG, 2022.

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46

Kondratiev, V., ed. Resources-based modernization model: opportunities and constraints. Primakov National Research Institute of World Economy and International Relations, Russian Academy of Sciences (IMEMO), 23, Profsoyuznaya Str., Moscow, 117997, Russian Federation, 2020. http://dx.doi.org/10.20542/978-5-9535-0575-8.

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This monograph examines the current trends in globalization of various economic sectors, which open up prospects for new participants, countries and companies and additional sources of value creation. The behavior of companies in different global industry value chains and their response to emerging market challenges is under consideration. Revealed are the opportunities and drivers for new players, suppliers of components and services to join global value chains. The research is based on the analysis of industry practice, corporate competition, consumer trends and technology advances in developed and developing countries.
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47

Applications Of Discretetime Markov Chains And Poisson Processes To Air Pollution Modeling And Studies. Springer, 2012.

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48

Stochastic Models In The Life Sciences And Their Methods Of Analysis. Singapore, Hong Kong: World Scientific Publishing Co. Pvt. Ltd., 2019.

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49

Bremaud, Pierre. Discrete Probability - Models and Methods: Probability on Graphs and Trees, Markov Chains and Random Fields, Entropy and Coding. Springer, 2017.

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50

Brémaud, Pierre. Discrete Probability Models and Methods: Probability on Graphs and Trees, Markov Chains and Random Fields, Entropy and Coding. Springer International Publishing AG, 2017.

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