In many everyday decisions, people quickly integrate noisy samples of information to form a preference among alternatives that offer uncertain rewards. Here, we investigated this decision process using the Flash Gambling Task (FGT), in which participants made a series of choices between a certain payoff and an uncertain alternative that produced a normal distribution of payoffs. For each choice...

Estimation of pest density is a basic requirement for integrated pest management in agriculture and forestry, and efficiency in density estimation is a common goal. Sequential sampling techniques promise efficient sampling, but their application can involve cumbersome mathematics and/or intensive warm-up sampling when pests have complex within- or between-site distributions. We provide tools fo...

The negative correlation between speed and accuracy in perceptual decision making is often explained as a tradeoff, where lowered decision boundaries under time pressure result in faster but more error-prone responses. Corresponding implementations in sequential sampling models confirmed the success of this account, which has led to the prevalent assumption that a second component of decision m...

Representing against population with a subset of it is termed as sampling. Sampling can either be statistical or non-statistical. In statistical sampling (probability sampling technique) calculating the probability of getting any particular sample is possible. It is scientific and every element stands an equal chance of being selected. In statistical sampling, workforce, time and money highly l...

We describe a new sequential sampling method for constrained multi-way tables, with foundations in linear programming and sequential normal sampling. The method builds on techniques from other sequential algorithms in a way that scales well and can handle more challenging data sets. We apply the new algorithm to data to demonstrate its efficiency.

There are two generations of Gibbs sampling methods for semiparametric models involving the Dirichlet process. The first generation suffered from a severe drawback: the locations of the clusters, or groups of parameters, could essentially become fixed, moving only rarely. Two strategies that have been proposed to create the second generation of Gibbs samplers are integration and appending a sec...

In this paper we discuss the design of sequential detection networks for nonparametric sequential analysis. We present a general probabilistic model for sequential detection problems where the sample size as well as the statistics of the sample can be varied. A general sequential detection network handles three decisions. First, the network decides whether to continue sampling or stop and make ...

Representing against population with a subset of it is termed as sampling. Sampling can either be statistical or non-statistical. In statistical sampling (probability sampling technique) calculating the probability of getting any particular sample is possible. It is scientific and every element stands an equal chance of being selected. In statistical sampling, workforce, time and money highly l...