There are two main approaches in compressed sensing: the geometric approach and the combinatorial approach. In this paper we introduce an information theoretic approach and use results from the theory of Huffman codes to construct a sequence of binary sampling vectors to determine a sparse signal. Unlike other approaches, our approach is adaptive in the sense that each sampling vector depends o...

Sequential sampling strategies have been developed for managing complexity when using computationally expensive computer simulations in engineering design. However, much of the literature has focused on objective-oriented sequential sampling methods for deterministic optimization. These methods cannot be directly applied to robust design which must account for uncontrollable variations in certa...

Color reproduction stabilization is a control process to ensure that the printed output matches the desired color tones. In this paper we consider the stabilization of single color tone separation. The mapping of input-tone to output-color is characterized by the color tone reproduction curve(TRC). By compensating for any variations of the Xerographic printing process, the TRC stabilization con...

J. Ecan. Entomal. 76: ]39-]43 (]983) A variable-intensity sampling method was developed to efficiently monitor all cabbage pests in commercial fields in New York while concentrating on the most important pests, Pieris rapae (L.) and Trichoplusia ni (Hiibner). The field is walked in a V-shaped pattern which is divided into 10 segments. Along each segment, one to four randomly selected cabbage pl...

We model the joint distribution of choice probabilities and decision times in binary choice tasks as the solution to a problem of optimal sequential sampling, where the agent is uncertain of the utility of each action and pays a constant cost per unit time for gathering information. In the resulting optimal policy, the agent’s choices are more likely to be correct when the agent chooses to deci...

Decisions take time, and as a rule more difficult decisions take more time. But this only raises the question of what consumes the time. For decisions informed by a sequence of samples of evidence, the answer is straightforward: more samples are available with more time. Indeed, the speed and accuracy of such decisions are explained by the accumulation of evidence to a threshold or bound. Howev...

We develop a sequential sampling procedure for a class of stochastic programs. We assume that a sequence of feasible solutions with an optimal limit point is given as input to our procedure. Such a sequence can be generated by solving a series of sampling problems with increasing sample size, or it can be found by any other viable method. Our procedure estimates the optimality gap of a candidat...

Two sequential methods are described for sampling constrained binary sequences from partial solutions. The backward method computes elimination ideals over finite fields and constructs partial solutions that extend. The forward method uses numerical global optimization to determine which partial solutions extend. The methods are applied to restricted orderings, binary dynamics, and random graphs.

In this paper, we apply sequential decision theory for scheduling tests in genetic algorithms and investigate an efficient sampling procedure for improving its performance. We use a loss function specifically defined for our analysis and obtain sequential decision equations for the optimal procedure. We derive simplified equations so that the procedure can be applied in practice. Finally, we co...

We propose a Bayesian decision-theoretic model of a fully sequential experiment in which the real-valued primary end point is observed with delay. The goal is to identify the sequential experiment which maximises the expected benefits of technology adoption decisions, minus sampling costs. The solution yields a unified policy defining the optimal ‘do not experiment’/‘fixed sample size experimen...