We show that evolution can be modelled as fitting a probability model using standard Markov-chain Monte-Carlo (MCMC) sampling. With some care, ‘genetic algorithms’ can be constructed that are reversible Markov chains that satisfy detailed balance; it follows that the stationary distribution of populations is a Gibbs distribution in a simple factorised form. For some standard and popular nonpara...

We propose a new selection procedures in unequal probability sampling that can be used with Horvitz and Thompson Estimator. Expression for inclusion probability for ith unit ( i  ) and joint inclusion probability of ith and jth unit ( ij  ) has been obtained. Some desirable properties of i  and ij  has been verified for proposed procedure.

The substitution of a nonresponding unit with one not originally selected in the sample is a commonly used method for dealing with unit nonresponse. Although frequently used in practice, substitution is largely neglected in the survey sampling literature. To date, few studies have attempted to develop a formal framework for describing and evaluating substitution methods, and little research has...

In this paper we introduce a new dynamic importance sampling propagation algorithm for Bayesian networks. Importance sampling is based on using an auxiliary sampling distribution from which a set of configurations of the variables in the network is drawn, and the performance of the algorithm depends on the variance of the weights associated with the simulated configurations. The basic idea of d...

A new methodology is proposed for generating realizations of a random vector with values in a finite-dimensional Euclidean space that are statistically consistent with a dataset of observations of this vector. The probability distribution of this random vector, while a-priori not known, is presumed to be concentrated on an unknown subset of the Euclidean space. A random matrix is introduced who...

The risk involved in basing a nematode management decision on predicted crop loss is related to the uncertainty in the crop damage function and error in measuring nematode population density. The sampling intensity necessary to measure a nematode population with specified precision varies with population density. Since the density is unknown prior to sampling, optimum sampling intensity for a m...

The Ziggurat Algorithm is a very fast rejection sampling method for generating PseudoRandom Numbers (PRNs) from statistical distributions. In the algorithm, rectangular sampling domains are layered on top of each other (resembling a ziggurat) to encapsulate the desired probability density function. Random values within these layers are sampled and then returned if they lie beneath the graph of ...

the aim of this study was to evaluate suicide probability in iranian males with substance abuse or dependence disorder and to investigate the predictors of suicide probability based on trait mindfulness, reasons for living and severity of general psychiatric symptoms.participants were 324 individuals with substance abuse or dependence in an outpatient setting and prison. reasons for living ques...

BACKGROUND The National Children's Study (NCS) was established as a national probability sample of births to prospectively study children's health starting from in utero to age 21. The primary sampling unit was 105 study locations (typically a county). The secondary sampling unit was the geographic unit (segment), but this was subsequently perceived to be an inefficient strategy. METHODS AND ...

Because of high costs or complex logistics, scientific populations cannot be measured entirely and must be sampled. Accepted scientific practice holds that sample selection be based on statistical principles to assure objectivity when estimating totals and variances. Probability sampling--obtaining samples with known probabilities--is the only method that assures these results. However, probabi...