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Showing 1 to 20 of 155 for “"Markov Chains"”.
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Boundary Theory for Markov Chains
Made available in DSpace on 2014-12-09T22:17:54Z (GMT). No. of bitstreams: 1 7000905.pdf: 3032865 bytes, checksum: fc1221d1aeeecc63651d7c505357f24a (MD5) Previous issue date: 1969
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Distributed analysis of Markov chains
… question are state transition systems from which Markov chains can be derived. Both phases of the analysis pipeline are considered: state space generation from a state transition model to form the Markov chain and finding performance information by solving the steady state equations of the Markov …
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Exact sampling with Markov chains
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mathematics, 1996.
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Markov Chains Derived From Lagrangian Mechanical Systems
The theory of Markov chains with countable state spaces is a greatly developed and successful area of probability theory and statistics. There is much interest in continuing to develop the theory of Markov chains beyond countable state spaces. One needs good and well motivated model systems in this …
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Reciprocal classes of continuous time Markov Chains
In this thesis we study reciprocal classes of Markov chains. Given a continuous time Markov chain on a countable state space, acting as reference dynamics, the associated reciprocal class is the set of all probability measures on path space that can be written as a mixture of its bridges. These …
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Rational inattention in control of Markov chains
This thesis poses a general model for optimal control subject to information constraint, motivated in part by recent work on information-constrained decision-making by economic agents. In the average-cost optimal control framework, the general model introduced in this paper reduces to a variant of …
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Variance-reduced simulation of lattice Markov chains
… of Monte Carlo estimation for lattice-valued Markov chains. We achieve this goal by manipulating the random inputs to stochastic processes (Poisson random variables in the discrete-time setting and Poisson processes in continuous-time) such that they become negatively correlated with some of …
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A Combinatorial Approach to Nearly Uncoupled Markov Chains
A discrete-time Markov chain on a state space S is a sequence of random variables X = fx0; x1; : : :g that take on values in S. A Markov chain is a model of a system which changes or evolves over time; the random variable xt is the state of the system at time t. A subset E � S is referred to as an …
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Interval Markov chains: performance measures and sensitivity analysis.
There is a vast literature on Markov chains where point estimates of transition and initial probabilities are used to calculate various performance measures. However, using these point estimates does not account for the associated uncertainty in estimate. If these point estimates are used, then the …
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Nearly reducible finite Markov chains: theory and algorithms
Finite Markov chains are probabilistic network models that are commonly used as representations of dynamical processes in the physical sciences, biological sciences, economics, and elsewhere. Markov chains that appear in realistic modelling tasks are frequently observed to be nearly reducible, …
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Markov Chains as Tools for Jazz Improvisation Analysis
… a statistical analysis and modeling technique (Markov chains) for the modeling of jazz improvisation with the intended subobjective of providing increased insight into an improviser's style and creativity through the postulation of quantitative measures of style and creativity based on the …
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Statistical inference from dependent data : networks and Markov chains
… distributions which result from imposing a Markovian property on the joint distribution of the data, namely Markov Random Fields (MRFs) and Markov chains. On MRFs, we explore a quantification for the amount of dependence and we strengthen previously known measure concentration results under …
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A characterization of the tail _-field for certain Markov chains
… state, recurrent, aperiodic and irreducible Markov Chain with stationary probabilities, the measure of any set of the tail a-field is equal to either zero or one. Although recurrent Markov chains have trivial tail a-fields this is not in general true for transient chains. However the tail …
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HIGH-ORDER MARKOV CHAINS IN COMPLEX NETWORKS: MODELS AND APPLICATIONS
… of complex networks by using approaches based on Markov and high-order Markov models. Regarding the structure of complex networks, we address the problem of the presence of three-body correlations between the node degrees in networks. Namely, we introduce measures to evaluate three-body …
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Model reduction of Markov chains with applications to building systems
Markov chain serves as an important modeling framework in applied science and engineering. e.g., Markov Chain Monte Carlo methods and Markov Decision Processes. A fundamental problem of Markov chain models is that the dimension of the problem could be very large in practice. This causes immense …
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Hidden Markov chains : convergence rates and the complexity of inference
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mathematics, 1993.
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