Nested sampling for physical scientists
G Ashton, N Bernstein, J Buchner, X Chen… - Nature Reviews …, 2022 - nature.com
Abstract This Primer examines Skilling's nested sampling algorithm for Bayesian inference
and, more broadly, multidimensional integration. The principles of nested sampling are …
and, more broadly, multidimensional integration. The principles of nested sampling are …
A survey of Monte Carlo methods for parameter estimation
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 …
of interest given a set of observed data. These estimates are typically obtained either by …
[HTML][HTML] A robust TOPSIS method for decision making problems with hierarchical and non-monotonic criteria
S Corrente, M Tasiou - Expert Systems with Applications, 2023 - Elsevier
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) …
method, namely the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) …
Derivative-free optimization: a review of algorithms and comparison of software implementations
LM Rios, NV Sahinidis - Journal of Global Optimization, 2013 - Springer
This paper addresses the solution of bound-constrained optimization problems using
algorithms that require only the availability of objective function values but no derivative …
algorithms that require only the availability of objective function values but no derivative …
[图书][B] Handbook of monte carlo methods
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 …
techniques, and real-world applications More and more of today's numerical problems found …
Dark energy
Dark Energy - IOPscience This site uses cookies. By continuing to use this site you agree to
our use of cookies. To find out more, see our Privacy and Cookies policy. Close this notification …
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Markov chains for exploring posterior distributions
L Tierney - the Annals of Statistics, 1994 - JSTOR
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 …
important examples are the Gibbs sampler and the Metropolis algorithm. In addition, several …
Lower bounds for covering times for reversible Markov chains and random walks on graphs
DJ Aldous - Journal of Theoretical Probability, 1989 - Springer
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 …
cN log (N), where c> 0 is an absolute constant. This is deduced from a more general result …
Sortilin is essential for proNGF-induced neuronal cell death
A Nykjaer, R Lee, KK Teng, P Jansen, P Madsen… - Nature, 2004 - nature.com
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 …
receptors,, and is expressed in a variety of tissues, notably brain, spinal cord and muscle. It …
[图书][B] A guide to simulation
P Bratley, BL Fox, LE Schrage - 2011 - books.google.com
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 …
Markov-chain simulation (Sections 1. 3, 2. 6, 3. 6, 4. 3, 5. 4. 5, and 5. 5);• gradient estimation …