UCT has recently emerged as an exciting new adversarial reasoning technique based on cleverly balancing exploration and exploitation in a Monte-Carlo sampling setting. It has been particularly successful in the game of Go but the reasons for its success are not well understood and attempts to replicate its success in other domains such as Chess have failed. We provide an in-depth analysis of th...

The few distance sampling studies that use Bayesian methods typically consider only line transect sampling with a half-normal detection function. We present a Bayesian approach to analyse distance sampling data applicable to line and point transects, exact and interval distance data and any detection function possibly including covariates affecting detection probabilities. We use an integrated ...

Level-crossing sampling refers to a sampling method that generates a sequence of timing samples when a signal crosses a specified set of levels. Conventional sampling techniques more typically rely on producing a sequence of amplitude samples at a specified set of timings. Level-crossing sampling has been considered as a promising alternative to time sampling when it is difficult to implement h...

Instance sampling is a method to balance extremely skewed training sets as they occur, for example, in machine learning settings for anaphora resolution. Here, the number of negative samples (i.e. non-anaphoric pairs) is usually substantially larger than the number of positive samples. This causes classifiers to be biased towards negative classification, leading to subopti-

In this article, we propose several quantization based stratified sampling methods to reduce the variance of a Monte-Carlo simulation. Theoretical aspects of stratification lead to a strong link between the problem of optimal Lquantization of a random variable and the variance reduction that can be achieved. We first emphasize on the consistency of quantization for designing strata in stratifie...

Let B be an unknown linear evolution process on C ' `(Zd) driving an unknown initial state x and producing the states {Bx, ` = 0, 1, . . .} at different time levels. The problem under consideration in this paper is to find as much information as possible about B and x from the measurements Y = {x(i), Bx(i), . . . , Bix(i) : i ∈ Ω ⊂ Z}. If B is a “low-pass” convolution operator, we show that we ...

Previous studies showed that olfactory cues are important for mammalian communication. However, many specific compounds that convey information between conspecifics are still unknown. To understand mechanisms and functions of olfactory cues, olfactory signals such as volatile compounds emitted from individuals need to be assessed. Sampling of animals with and without scent glands was typically ...

Ecological examples provide a rich source of material for class exercises as part of a course on sampling methods. These examples can be used for teaching the classical theory for the estimation of the parameters of a finite population, in which case they help students to learn the practical aspects of the design of studies, and the appropriate use of the standard equations for estimation with ...

We consider the problem of approximate belief-state monitoring using particle filtering for the purposes of implementing a policy for a partially observable Markov decision process (POMDP). While particle filtering has become a widely used tool in AI for monitoring dynamical systems, rather scant attention has been paid to their use in the context of decision making. Assuming the existence of a...