منابع مشابه
Numerical Methods in Markov Chain Modeling
This paper describes and compares several methods for computing stationary probability distributions of Markov chains. The main linear algebra problem consists of computing an eigenvector of a sparse, non-symmetric, matrix associated with a known eigenvalue. It can also be cast as a problem of solving a homogeneous, singular linear system. We present several methods based on combinations of Kry...
متن کاملMarkov chain Monte Carlo methods in biostatistics.
Appropriate models in biostatistics are often quite complicated. Such models are typically most easily fit using Bayesian methods, which can often be implemented using simulation techniques. Markov chain Monte Carlo (MCMC) methods are an important set of tools for such simulations. We give an overview and references of this rapidly emerging technology along with a relatively simple example. MCM...
متن کاملAdvances in Markov chain Monte Carlo methods
Probability distributions over many variables occur frequently in Bayesian inference, statistical physics and simulation studies. Samples from distributions give insight into their typical behavior and can allow approximation of any quantity of interest, such as expectations or normalizing constants. Markov chain Monte Carlo (MCMC), introduced by Metropolis et al. (1953), allows sampling from d...
متن کاملNumerical Methods for the Markov Functional Model
a4screenshowBGM modelJanuary 2005StandardNoneSimon Johnson+44 20 [email protected]<co...
متن کاملMarkov Chain Methods for Analyzing Urban Networks
Complex transport networks abstracted as graphs (undirected, directed, or multicomponent) can be effectively analyzed by random walks (or diffusions). We have unified many concepts into one framework and studied in details the structural and spectral properties of spatial graphs for five compact urban patterns.
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: Operations Research
سال: 1992
ISSN: 0030-364X,1526-5463
DOI: 10.1287/opre.40.6.1156