The phrase "a joint probability density" is correct and usable in written English.
It can be used in contexts related to statistics, probability theory, or data analysis when discussing the probability distribution of two or more random variables simultaneously.
Example: "To analyze the relationship between the two variables, we need to calculate a joint probability density."
Alternatives: "a joint probability distribution" or "a combined probability density".
Structural reliability analysis is conventionally based on a description of uncertainty via a joint probability density function (JPDF).
The transition probability distribution ( p underline {r}_{n}|underline {c}_{n})) is a joint probability density function (PDF) conditioned on the CPE code symbol (underline {c}_{n}).
We incorporated a joint probability density estimation and the Wilcoxon rank sum test to investigate the relationship between tree species and dead/live tree status for roost tree selection.
By taking the Tsing Ma Bridge in Hong Kong as an example, a joint probability density function of wind speed and direction is first established based on wind data recorded by the WASHMS installed in the bridge.
We consider correlations induced by the use of rate rules for modeling reaction rate constants, as well as those resulting from fitting rate expressions to empirical measurements arriving at a joint probability density for all Arrhenius parameters.
In order to estimate the filter power spectrum, a joint probability density function of the filter response co-ordinates at two different times is derived using an orthogonal expansion technique together with the Fokker –Planck equation.
In the first approach, a PC representation is constructed by matching a target joint probability density function (pdf) based on sequential conditioning (a sequence of conditional probability relations) in conjunction with the Rosenblatt transformation.
We design a coupled shape prior to model the transformation between a related pair of surfaces, using a nonparametric joint probability density estimation.
The present paper first gives a brief review of procedures to generate two or more correlated random variables with Gaussian distributions and with a given joint probability density.
The independence between source vectors is approximated by a multivariate, joint probability density function (pdf) of the components from each source vector, and the joint pdf is maximized rather than the individual independencies between each frequency bin.
A transported joint probability density function (pdf) approach was applied to model three (Re≅8200, 22,400, and 44,800) piloted CH4/O2/N2 turbulent jet diffusion flames investigated experimentally by Barlow and co-workers.
