# PPDF

(redirected from Posterior probability density function)
AcronymDefinition
PPDFPosterior Probability Density Function (reliability)
PPDFPacific Provider Development Fund (Lottery Grants Board; New Zealand)
PPDFPortsmouth Percussive Dance Festival (Portsmouth, NH)
PPDFPonderosa Pine/Douglas Fir (forest)
References in periodicals archive ?
Therefore, measurement rate is modeled by GAM(*) in order to obtain the posterior probability density function of [[gamma].sub.k] :
Therefore, N(*) and IW(*) are proposed to describe [x.sub.k] and [X.sub.k] separately , and the posterior probability density function is a GGIW distribution; namely,
Finally, we believe that in one form or another, the full posterior probability density function needs to be conveyed to the public.
In this report we will display directly standardized plots of hospital posterior probability density functions for 30-day mortality under the HC model (without hospital characteristics) and then our expanded model.
where [mathematical expression not reproducible] is the posterior probability density function expectation of [B.sup.t] at t stage.
An objective of particle filter (PF), as the Bayesian estimator, is to approximate a conditional posterior probability density function, p(x(k)|[Z.sub.k]), where x(k) is vector of states at time instant k, and [Z.sub.k] = {z(1),, ..., z(k)} is set of observations until time instant k.
The key idea is to represent the target posterior probability density function (PDF) of the state given the observations by a set of random particles with their associated weights.
from the posterior probability density function, it is usually necessary to integrate the right-hand side of Equation (1) with respect to [theta].
The following cyclic procedure describes a simple case with two parameters [alpha] and [beta] having joint posterior probability density function Df([alpha], [beta]|D).
From a Bayesian point of view, the solution is to recursively obtain the a posterior probability density function p([x.sub.0:k]|[z.sub.1:k]) of states at time k given all available measurements.
The joint posterior probability density function of the reaction rates at sampling times ranging from 6 to 137 d was evaluated as follows:
The best-fitting model from the likelihood-based GLMM was used to estimate Bayesian posterior probability density functions for each model parameter, which were then combined with the GIS output to estimate probability density functions for total geoduck abundance in each region of Hood Canal.
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