This paper develops a new modified Bayesian Kriging (MBKG) surrogate modeling method for problems in which simulation analyses are inherently noisy and thus standard Kriging approaches fail to properly represent the responses. The purpose is to develop a method that can be used to carry out reliability analysis to predict probability of failure. The formulation of the MBKG surrogate modeling me...

Sequential importance sampling algorithms have been defined to estimate likelihoods in models of ancestral population processes. However, these algorithms are based on features of the models with constant population size, and become inefficient when the population size varies in time, making likelihood-based inferences difficult in many demographic situations. In this work, we modify a previous...

The cognitive and neuronal mechanisms of perceptual decision making have been successfully linked to sequential sampling models. These models describe the decision process as a gradual accumulation of sensory evidence over time. The temporal evolution of economic choices, however, remains largely unexplored. We tested whether sequential sampling models help to understand the formation of value-...

In this paper, we address the problem of sequential Bayesian model selection. This problem does not usually admit any closed-form analytical solution. We propose here an original sequential simulation-based method to solve the associated Bayesian computational problems. This method combines sequential importance sampling, a resampling procedure and reversible jump MCMC moves. We describe a gene...

Two sets of sequential presence-absence sampling plans for the management of diamondback moth (DBM) were developed and evaluated, one for the classification of levels of the proportions of plants infested with larvae and the other for the classification of levels of larval density. The action thresholds investigated were 0.15, 0.25, 0.35 and 0.45 for proportion-based sampling plans and 0.2, 0.4...

The purpose of this paper is to derive optimal rules for sequential mastery tests. In a sequential mastery test, the decision is to classify a subject as a master, a nonmaster, or to continue sampling and administering another random item. The framework of minimax sequential decision theory (minimum information approach) is used; that is, optimal rules are obtained by minimizing the maximum exp...

2 Lihelihood inference using importance sampling algorithms 3 2.1 Inferring the likelihood for a parameter point by importance sampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 2.1.1 Sequential importance sampling formulation . . . . . . 3 2.1.2 Optimal p and w . . . . . . . . . . . . . . . . . . . . . . . 5 2.1.3 Formulation of efficient p and w . . . . . . . . . . . ....

Importance sampling, particularly sequential and adaptive importance sampling, have emerged as competitive simulation techniques to Markov–chain Monte–Carlo techniques. We compare importance sampling and the Metropolis algorithm as two ways of changing the output of a Markov chain to get a different stationary distribution.

Statistical selection procedures are used to select the best of a finite set of alternatives, where “best” is defined in terms of each alternative’s unknown expected value, and the expected values are inferred through statistical sampling. One effective approach, which is based on a Bayesian probability model for the unknown mean performance of each alternative, allocates samples based on maxim...

The tomato-potato psyllid Bactericera cockerelli (Sulc) (Hemiptera: Triozidae) is a pest of many solanaceous plants, including tomato (Solanum lycopersicum L.) and potato (Solanum tuberosum L.). In tomato, feeding by nymphs is associated with "psyllid yellows." B. cockerelli also vectors "Candidatus Liberibacter psyllaurous," an infectious bacterium that causes "vein greening" disease. Decision...