Abstract This Primer examines Skilling's nested sampling algorithm for Bayesian inference
and, more broadly, multidimensional integration. The principles of nested sampling are …

Statistical signal processing applications usually require the estimation of some parameters
of interest given a set of observed data. These estimates are typically obtained either by …

This paper introduces an extension of a well-known Multiple Criteria Decision Aiding
method, namely the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) …

This paper addresses the solution of bound-constrained optimization problems using
algorithms that require only the availability of objective function values but no derivative …

A comprehensive overview of Monte Carlo simulation that explores the latest topics,
techniques, and real-world applications More and more of today's numerical problems found …

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Several Markov chain methods are available for sampling from a posterior distribution. Two
important examples are the Gibbs sampler and the Metropolis algorithm. In addition, several …

For simple random walk on a N-vertex graph, the mean time to cover all vertices is at least
cN log (N), where c> 0 is an absolute constant. This is deduced from a more general result …

Sortilin (∼ 95 kDa) is a member of the recently discovered family of Vps10p-domain
receptors,, and is expressed in a variety of tissues, notably brain, spinal cord and muscle. It …

Changes and additions are sprinkled throughout. Among the significant new features are:•
Markov-chain simulation (Sections 1. 3, 2. 6, 3. 6, 4. 3, 5. 4. 5, and 5. 5);• gradient estimation …