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...

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...

Designing an efficient sampling scheme for a rare and clustered population is a challenging area of research. Adaptive cluster sampling, which has been shown to be viable for such a population, is based on sampling a neighborhood of units around a unit that meets a specified condition. However, the edge units produced by sampling neighborhoods have proven to limit the efficiency and applicabili...

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.

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...

The ability to simulate graphs with given properties is important for the analysis of social networks. Sequential importance sampling has been shown to be particularly effective in estimating the number of graphs adhering to fixed marginals and in estimating the null distribution of test statistics. This paper builds on the work of Chen et al. (2005), providing an intuitive explanation of the s...