Approximating Markov chains.
نویسندگان
چکیده
منابع مشابه
On Approximating the Stationary Distribution of Time-reversible Markov Chains
Approximating the stationary probability of a state in a Markov chain through Markov chain Monte Carlo techniques is, in general, inefficient. Standard random walk approaches require Õ(τ/π(v)) operations to approximate the probability π(v) of a state v in a chain with mixing time τ , and even the best available techniques still have complexity Õ(τ/π(v)); and since these complexities depend inve...
متن کاملEmpirical Bayes Estimation in Nonstationary Markov chains
Estimation procedures for nonstationary Markov chains appear to be relatively sparse. This work introduces empirical Bayes estimators for the transition probability matrix of a finite nonstationary Markov chain. The data are assumed to be of a panel study type in which each data set consists of a sequence of observations on N>=2 independent and identically dis...
متن کاملApproximating labelled Markov processes
Labelled Markov processes are probabilistic versions of labelled transition systems. In general, the state space of a labelled Markov process may be a continuum. In this paper, we study approximation techniques for continuous-state labelled Markov processes. We show that the collection of labelled Markov processes carries a Polish-space structure with a countable basis given by finite-state Mar...
متن کاملApproximating Labeled Markov Processes
We study approximate reasoning about continuous-state labeled Markov processes. We show how to approximate a labeled Markov process by a family of finite-state labeled Markov chains. We show that the collection of labeled Markov processes carries a Polish space structure with a countable basis given by finite state Markov chains with rational probabilities. The primary technical tools that we d...
متن کاملMarkov chains
[Tip: Study the MC, QT, and Little's law lectures together: CTMC (MC lecture), M/M/1 queue (QT lecture), Little's law lecture (when deriving the mean response time from mean number of customers), DTMC (MC lecture), M/M/1 queue derivation using DTMC analysis, derive distribution of response time in M/M/1 queue (QT lecture), relation between Markov property and mem-oryless property (MC lecture), ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Proceedings of the National Academy of Sciences
سال: 1992
ISSN: 0027-8424,1091-6490
DOI: 10.1073/pnas.89.10.4432