We establish quantitative bounds for rates of convergence and asymptotic variances for iterated conditional sequential Monte Carlo (i-cSMC) Markov chains and associated particle Gibbs samplers [1]. Our main findings are that the essential boundedness of potential functions associated with the i-cSMC algorithm provide necessary and sufficient conditions for the uniform ergodicity of the i-cSMC M...

Metropolis-Hastings Markov chain Monte Carlo (MCMC) algorithms and other popular MCMC algorithms induce a Markov chain which has the target distribution as its stationary distribution. Optimal scaling refers to the need to tune the parameters of the proposal kernel in order to ensure the Markov chain obtained from the algorithm converges as fast as possible to stationarity. Theoretical results ...

in this paper a simple and effective expert system to predict random data fluctuation in short-term period is established. evaluation process includes introducing fourier series, markov chain model prediction and comparison (gray) combined with the model prediction gray- fourier- markov that the mixed results, to create an expert system predicted with artificial intelligence, made this model to...

Markov chain Monte Carlo (e. g., the Metropolis algorithm and Gibbs sampler) is a general tool for simulation of complex stochastic processes useful in many types of statistical inference. The basics of Markov chain Monte Carlo are reviewed, including choice of algorithms and variance estimation, and some new methods are introduced. The use of Markov chain Monte Carlo for maximum likelihood est...

Internetware system are widely used in the chemical and pharmaceutical industry, effective acquisition of transition probability matrix is directly related to internetware reliability computation based on Markov chain model. The construction of Markov chain and the acquisition of transition probability of internetware are studied; the Markov chain of internetware reliability is constructed. Tra...

in this paper, the ambiguity of nite state irreducible markov chain trajectories is reminded and is obtained for two state markov chain. i give an applicable example of this concept in president election

Multimodal, high-dimension posterior distributions are well known to cause mixing problems for standard Markov chain Monte Carlo (MCMC) procedures; unfortunately such functional forms readily occur in empirical political science. This is a particularly important problem in applied Bayesian work because inferences are made from finite intervals of the Markov chain path. To address this issue, we...

with recent developments in communication technologies, it is possible to access information any time and in any place; however, mobile environments bring forward certain requirements and exclusive challenges including low connection speeds and bandwidth limitations. thus, we need to consider mobile limitations when using web services. providing services and limited resource allocation to users...

growing amount of information on biological sequences has made application of statistical approaches necessary for modeling and estimation of their functions. in this paper, sensitivity and specificity of the first and second markov chains for prediction of genes was evaluated using the complete double stranded  dna virus. there were two approaches for prediction of each markov model parameter,...