The phrase "a posterior probability distribution" is correct and usable in written English.
It can be used in contexts related to statistics, Bayesian inference, or probability theory when discussing the distribution of a variable after observing evidence.
Example: "To update our beliefs about the parameter, we need to calculate the a posterior probability distribution based on the new data."
Alternatives: "posterior distribution" or "updated probability distribution".
Sample information is then obtained and combined through an application of Bayes's theorem to provide a posterior probability distribution for the parameter.
The estimation procedure provides a posterior probability distribution that integrates prior estimates based on the knowledge of the process, and the likelihood of occurrence based on historical data.
WofE is based on a log-linear form of Bayes' rule and uses the prior probability distribution and the likelihood of the data to generate a posterior probability distribution.
For each test RoI r, the network outputs a posterior probability distribution p and a series of predicted bbox offsets relative to r (each of the u classes gets its own refined bounding-box prediction).
The inference runs from a so-called prior probability distribution over statistical hypotheses, which expresses the degree of belief in the hypotheses before data has been collected, to a posterior probability distribution over the hypotheses, which expresses the beliefs after the data have been incorporated.
This yields a posterior probability distribution for velocity (Fig. 4A).
Taking into account assessment results (such as disaster economic loss and casualty data) published by the authorities as a point of departure, we can get a true posterior probability distribution from an actual assessment and construct an error function.
Thus, in agreement with Huelsenbeck et al. [4], it would seem that the data contains some information over the rate of transformation and that a prior distribution generating a skewed posterior probability distribution is not appropriate given the data.
The advantage of a full probability model specification is that it produces a joint posterior probability distribution for the parameters, which allows for more flexible approach to inference and incorporates explicitly the uncertainty of estimation in all parameters.
In this context, Bayes's theorem provides a mechanism for combining a prior probability distribution for the states of nature with sample information to provide a revised (posterior) probability distribution about the states of nature.
Statistical models were sampled from a Bayesian posterior probability distribution.
