Bayesian analysis of a change-point in exponential families with applications The conjugate priors for the exponential families are considered in the analysis. The marginal posterior distribution of the change-point j is derived. ... CB Lee - 《Computational Statistics & Data Analysis》 被引量: 24...
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内容提示: A conjugate prior for the Dirichlet distributionJean-Marc AndreoliNaverlabs EuropeOctober 2014; last update October 2018AbstractThis note investigates a conjugate class for the Dirichlet distribution class in the exponential family.1 Basic def i nitionsThe exponential family of distributions is...
Conjugate Prior Distribution In subject area: Mathematics Conjugate prior distribution for the likelihood: Prior distribution of a random variable is called the conjugate prior distribution for its likelihood when both the posterior and the prior belong to the same distribution family. From: Chemometrics...
Imprecise conjugate prior densities for the oneparameter exponential family of distributions - Coolen - 1993 () Citation Context ...es a prior conjugate distribution with fixed ny and uses the neighborhood around this prior created by varying ny [2] (robust Bayesian literature). Another idea uses...
they usually allow us to derive a closed-form expression for the posterior distribution; they are easy to interpret, as we can easily see how the parameters of the prior change after the Bayesian update. Exponential families Conjugate priors are easily characterized when the distribution of ...
which represents the probability of gettings heads. Assume that the value 1 corresponds to heads, and the value 0 corresponds to tails. Then the distribution of the outcome of the coin flip looks like this: It turns out that the conjugate prior for the bernoulli distribution is something calle...
For example, if the protein in the DNA-protein conjugate is the first component of the complement cascade (Clq or Clqrs) and the DNA... JG Bruno,JC Miner - US 被引量: 12发表: 2005年 Estimation of Reliability of Cascade System Conjugate priorCascade systemExponential distributionMinimum ...
Gaussian Process is a non-parametric prior which can be understood as a distribution on the function space intuitively. It is known that by introducing appropriate prior to the weights of the neural networks, Gaussian Process can be obtained by taking the infinite-width limit of the Bayesian neur...
Cellular processes are often regulated by the dynamic distribution of molecules inside cells. Therefore, we first used cRGT for control of protein positioning. HeLa cells co-expressing EGFP-mito and mCherry-eDHFR were treated by 24 μM cRGT for 1.5 h and imaged without washing. cRGT direct...