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Markov Chains pp 138-171 | Cite as

Introduction to Continuous Time

  • David Freedman

Abstract

Let I be a finite or countably infinite set. A matrix M on I is a function (i, j) → M(i, j) from I × I to the real line. Call M stochastic iff M(i, j) ≧ 0 for all i and j, while Σ j M(i,j) = 1 for all i. Call M substochastic iff M(i, j) ≧ 0 for all i and j, while Σ j M(i, j) ≦ 1 for all i. Matrix multiplication is defined as usual:
$$ MN\left( {i,j} \right) = \sum\nolimits_{{k \in I}} {\,M\left( {i,k} \right)N\left( {k,j} \right)} $$
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Keywords

Stationary Transition Markov Chain Continuous Time Step Function Sample Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© David A. Freedman 1983

Authors and Affiliations

  • David Freedman
    • 1
  1. 1.Department of StatisticsUniversity of CaliforniaBerkeleyUSA

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