Coalescent likelihood is the probability of observing the given population sequences under the coalescent model. Computation of coalescent likelihood under the infinite sites model is a classic problem in coalescent theory. Existing methods are based on either importance sampling or Markov chain Monte Carlo. In this paper, we develop a simple method that can compute the exact coalescent likelih...

We give a Markov chain that converges to its stationary distribution very slowly. It has the form of a Gibbs sampler running on a posterior distribution of a parameter f3 given data X. Consequences for Gibbs sampling are discussed.

Effective relaxation processes for difficult systems like proteins or spin glasses require special simulation techniques that permit barrier crossing to ensure ergodic sampling. Numerous adaptations of the venerable Metropolis Monte Carlo (MMC) algorithm have been proposed to improve its sampling efficiency, including various hybrid Monte Carlo (HMC) schemes, and methods designed specifically f...

In dieser Arbeit werden Markov Chain Monte Carlo (MCMC) Methoden für das Sampling von Dependenzbäumen entwickelt. Dependenzbäume sind ein Formalismus für die syntaktische Annotation von natürlichsprachlichen Sätzen. Dieser Formalismus hat in den letzten Jahren immer mehr an Bedeutung gewonnen und zur gleichen Zeit sind die Modelle, die zur Generierung und Verarbeitung von Dependenzbäumen verwen...

Chain sampling plan for Log Logistic distribution when the life-test is truncated at a prespecified time is discussed. The design parameters such as the minimum sample size and the acceptance number necessary to assure a specified mean life time are obtained by satisfying the producer’s and consumer’s risks at the specified quality levels, under the assumption that the termination time and the ...

Over the past several years importance sampling in conjunction with regenerative simulation has been presented as a promising method for estimating reliability measures in highly dependable Markovian systems. Existing methods fail to provide benefits over crude Monte Carlo for the analysis of systems that contain significant component redundancies. This paper presents refined importance samplin...

The increase of international competition motivated most of the organizations to create useful shared mutual cooperation with supply chain partners since they understood that cooperation and collaboration of supply chain partners is the prerequisite for the increase of reliability level and the decrease of risks and also the enhancement of innovative qualities and profitability of the companies...

This paper makes two contributions to the computational geometry of decomposable graphs, aimed primarily at facilitating statistical inference about such graphs where they arise as assumed conditional independence structures in stochastic models. The first of these provides sufficient conditions under which it is possible to completely connect two disconnected cliques of vertices, or perform th...

I describe algorithms for drawing from distributions using adaptive Markov chain Monte Carlo (MCMC) methods, introduce a Mata function for performing adaptive MCMC, amcmc(), and a suite of functions amcmc *() allowing an alternative implementation of adaptive MCMC. amcmc() and amcmc *() may be used in conjunction with models set up to work with Mata’s [M-5] moptimize( ) or [M-5] optimize( ), or...

The problem of efficiently sampling from a set of (undirected) graphs with a given degree sequence has many applications. One approach to this problem uses a simple Markov chain, which we call the switch chain, to perform the sampling. The switch chain is known to be rapidly mixing for regular degree sequences. We prove that the switch chain is rapidly mixing for any degree sequence with minimu...