Results 221 to 230 of about 186,614 (271)
Some of the next articles are maybe not open access.

Related searches:

Continuous-time Markov chains

2014
We present here a short summary of the parts of the theory of Markov processes with countable state space that is used in the chapters describing stochastic models of populations. The presentation will be restricted to Markov process that are generated by an infinitesimal transition matrix, as discussed below.
Mimmo Iannelli, Andrea Pugliese
  +4 more sources

Continuous-time Markov chains

2012
We generalize the processes discussed in the last three chapters even further by allowing an instantaneous transition from any state into any other (meaning different) state, with each such transition having its own specific rate. These new processes are so much more difficult to investigate that we are forced to abandon the infinite-state space and ...
Jan Vrbik, Paul Vrbik
  +4 more sources

Finite Continuous Time Markov Chains

Theory of Probability & Its Applications, 1961
In a recent book by the authors (see footnote on page 101) a systematic method was developed for computing the basic descriptive quantities of finite Markov chains. Matrix expressions were obtained for these quantities in terms of certain basic matrices, easily obtainable from the transition matrix.In this note, corresponding expressions are given for ...
Kemeny, J. G., Snell, J. L.
openaire   +2 more sources

Continuous-Time Markov Chains

2018
We will write a language for specifying continuous-time Markov chains (CTMCs) and for computing the likelihood of parameters in such CTMCs given a trace of which states the chain is in at different time points. As with the list comprehension example in the previous chapter, we are not going to use operators to create a new syntax for the language but ...
  +5 more sources

Continuous — Time Markov Chains

1996
Poisson processes in Lesson 4 are examples of continuous-time stochastic processes (with discrete state spaces) having the Markov property in the continuous-time setting. In this Lesson, we discuss the probabilistic structure and some computational aspects of such processes with emphasis on Birth and Death chains.
Denis Bosq, Hung T. Nguyen
openaire   +1 more source

Continuous Time Markov Chains

2016
A queueing system can be described in terms of servers and a flow of clients who access servers and are served according to some pre-established rules. The clients after service can either stay in the system or leave it, also according to some established rules.
Francesca Biagini, Massimo Campanino
openaire   +1 more source

Continuous-Time Markov Chains

1992
In this chapter we again develop continuous-time stochastic processes having Markov properties.
openaire   +1 more source

Home - About - Disclaimer - Privacy