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词条 Markov renewal process
释义

  1. Definition

  2. Relation to other stochastic processes

  3. See also

  4. References and further reading

In probability and statistics a Markov renewal process is a random process that generalizes the notion of Markov jump processes. Other random processes like Markov chain, Poisson process, and renewal process can be derived as a special case of an MRP (Markov renewal process).

Definition

Consider a state space Consider a set of random variables , where are the jump times and are the associated states in the Markov chain (see Figure). Let the inter-arrival time, . Then the sequence is called a Markov renewal process if

Relation to other stochastic processes

  1. If we define a new stochastic process for , then the process is called a semi-Markov process. Note the main difference between an MRP and a semi-Markov process is that the former is defined as a two-tuple of states and times, whereas the latter is the actual random process that evolves over time and any realisation of the process has a defined state for any given time. The entire process is not Markovian, i.e., memoryless, as happens in a continuous time Markov chain/process (CTMC). Instead the process is Markovian only at the specified jump instants. This is the rationale behind the name, Semi-Markov.&91;1&93;&91;2&93;&91;3&93; (See also: hidden semi-Markov model.)
  2. A semi-Markov process (defined in the above bullet point) where all the holding times are exponentially distributed is called a CTMC. In other words, if the inter-arrival times are exponentially distributed and if the waiting time in a state and the next state reached are independent, we have a CTMC.
  3. The sequence in the MRP is a discrete-time Markov chain. In other words, if the time variables are ignored in the MRP equation, we end up with a DTMC.
  4. If the sequence of s are independent and identically distributed, and if their distribution does not depend on the state , then the process is a renewal process. So, if the states are ignored and we have a chain of iid times, then we have a renewal process.

See also

  • Markov process
  • Renewal theory
  • Variable-order Markov model
  • Hidden semi-Markov model
{{more footnotes|date=July 2012}}

References and further reading

1. ^{{cite book|last=Medhi|first=J.|title=Stochastic processes|year=1982|publisher=Wiley & Sons|location=New York|isbn=978-0-470-27000-4}}
2. ^{{cite book|last=Ross|first=Sheldon M.|title=Stochastic processes.|year=1999|publisher=Routledge.|location=New York [u.a.]|isbn=978-0-471-12062-9|edition=2nd}}
3. ^{{cite book|last=Barbu|first=Vlad Stefan|title=Semi-Markov chains and hidden semi-Markov models toward applications : their use in reliability and DNA analysis|year=2008|publisher=Springer|location=New York|isbn=978-0-387-73171-1|author2=Limnios, Nikolaos}}

1 : Markov processes
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