Publication

Metropolis-Hastings sampling of paths

Michel Bierlaire
2011
Reports

Abstract

We consider the previously unsolved problem of sampling cycle-free paths according to a given distribution from a general network. The problem is difficult because of the combinatorial number of alternatives, which prohibits a complete enumeration of all paths and hence also forbids to compute the normalizing constant of the sampling distribution. The problem is important because the ability to sample from a known distribution introduces mathematical rigor into many applications that range from route guidance to the estimation of choice models with sampling of alternatives.

Official source

https://infoscience.epfl.ch/entities/publication/b5febaeb-4644-4f65-8d66-69692c794ffe

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Michel Bierlaire
2011
Reports

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/b5febaeb-4644-4f65-8d66-69692c794ffe

About this result

Related concepts (32)

Gibbs sampling

In statistics, Gibbs sampling or a Gibbs sampler is a Markov chain Monte Carlo (MCMC) algorithm for obtaining a sequence of observations which are approximated from a specified multivariate probability distribution, when direct sampling is difficult. This sequence can be used to approximate the joint distribution (e.g., to generate a histogram of the distribution); to approximate the marginal distribution of one of the variables, or some subset of the variables (for example, the unknown parameters or latent variables); or to compute an integral (such as the expected value of one of the variables).

Sampling (statistics)

In statistics, quality assurance, and survey methodology, sampling is the selection of a subset or a statistical sample (termed sample for short) of individuals from within a statistical population to estimate characteristics of the whole population. Statisticians attempt to collect samples that are representative of the population. Sampling has lower costs and faster data collection compared to recording data from the entire population, and thus, it can provide insights in cases where it is infeasible to measure an entire population.

Sampling error

In statistics, sampling errors are incurred when the statistical characteristics of a population are estimated from a subset, or sample, of that population. It can produced biased results. Since the sample does not include all members of the population, statistics of the sample (often known as estimators), such as means and quartiles, generally differ from the statistics of the entire population (known as parameters). The difference between the sample statistic and population parameter is considered the sampling error.

Related publications (32)

Metropolis-Hastings sampling of paths

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