approximate likelihood
31Bayesian experimental design — provides a general probability theoretical framework from which other theories on experimental design can be derived. It is based on Bayesian inference to interpret the observations/data acquired during the experiment. This allows accounting for… …
32Social-circles network model — The generative model of feedback networks [Cited by Wei, Wang, Qiuping, Nivanen, Lauret et al (2006 01 12) [http://www.citebase.org/abstract?id=oai%3AarXiv.org%3Aphysics%2F0601091 How to fit the degree distribution of the air network?] ] , [Cited …
33radiation — radiational, adj. /ray dee ay sheuhn/, n. 1. Physics. a. the process in which energy is emitted as particles or waves. b. the complete process in which energy is emitted by one body, transmitted through an intervening medium or space, and… …
34Principle of maximum entropy — This article is about the probability theoretic principle. For the classifier in machine learning, see maximum entropy classifier. For other uses, see maximum entropy (disambiguation). Bayesian statistics Theory Bayesian probability Probability… …
35Nearest neighbor search — (NNS), also known as proximity search, similarity search or closest point search, is an optimization problem for finding closest points in metric spaces. The problem is: given a set S of points in a metric space M and a query point… …
36Health and Disease — ▪ 2009 Introduction Food and Drug Safety. In 2008 the contamination of infant formula and related dairy products with melamine in China led to widespread health problems in children, including urinary problems and possible renal tube… …
37Bayesian inference — is statistical inference in which evidence or observations are used to update or to newly infer the probability that a hypothesis may be true. The name Bayesian comes from the frequent use of Bayes theorem in the inference process. Bayes theorem… …
38Bayesian network — A Bayesian network, Bayes network, belief network or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional dependencies via a directed acyclic graph (DAG). For example …
39Robust statistics — provides an alternative approach to classical statistical methods. The motivation is to produce estimators that are not unduly affected by small departures from model assumptions. Contents 1 Introduction 2 Examples of robust and non robust… …
40Pearson's chi-squared test — (χ2) is the best known of several chi squared tests – statistical procedures whose results are evaluated by reference to the chi squared distribution. Its properties were first investigated by Karl Pearson in 1900.[1] In contexts where it is… …
