Noun:

যৌথ সম্ভাবনা,

joint probability's Usage Examples:

{\displaystyle X,Y,\ldots } , that are defined on a probability space, the joint probability distribution for X , Y , … {\displaystyle X,Y,\ldots } is a probability.

probability density function associated to the set as a whole, often called joint probability density function.

(which may be finitely or infinitely long) whose joint probability distribution does not change when the positions in the sequence in.

on the probability distribution of a statistic, rather than on the joint probability distribution of all the individual sample values.

This can be calculated by summing the joint probability distribution over all values of Y.

correlated uncertainty, multiple attributes may be described by a joint probability distribution.

theory, the Chapman–Kolmogorov equation is an identity relating the joint probability distributions of different sets of coordinates on a stochastic process.

target variable Y, a generative model is a statistical model of the joint probability distribution on X × Y, P ( X , Y ) {\displaystyle P(X,Y)} ; A discriminative.

conditional on the value of a sufficient statistic for a parameter, the joint probability distribution of the data does not depend on that parameter.

distribution of a stationary process or stationary time series The set of joint probability distributions of a stationary process or stationary time series In.

the joint probability of all random variables.

More precisely, if the events are X 1 , … , X n {\displaystyle X_{1},\ldots ,X_{n}} then the joint probability.

algorithm for obtaining a configuration of a local maximum of the joint probability of a Markov random field.

(\delta ,\nu ,{\boldsymbol {\lambda }},{\boldsymbol {\mu }})} , the joint probability density function (pdf) of Y = ( Y 1 , … , Y k ) {\displaystyle {\boldsymbol.

which is a population parameter that can be seen as a property of the joint probability distribution, and (2) the sample covariance, which in addition to.

}}P(B)\neq 0,} where P ( A ∩ B ) {\displaystyle P(A\cap B)} is the joint probability of both A and B being true.

the unknown joint probability distribution for x → {\displaystyle {\vec {x}}} and y {\displaystyle y} .

Without knowing the joint probability distribution.

To put it more formally, we assume that there is a joint probability distribution P ( x , y ) {\displaystyle P(x,y)} over X {\displaystyle.

When the joint probability density of the random variables is strictly positive, it is also referred.

For example, when teaching the joint probability of two independent events, teachers often explain the abstract procedure.

Synonyms:

chance; probability;

Antonyms:

impossibility; planned; improbability;