joint probability sentence in Hindi
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- However, the two tests are not completely redundant; as a result the joint probability of their rejection is less than ?.
- The joint probability distribution function " p ( C, T ) " represents the probability that two events occur simultaneously.
- After the solution attempt, the principle behind the joint probability of two independent events will be explained, using the chosen example.
- Teachers may then use the other examples in order to deepen the understanding of joint probabilities, for example by using worked examples.
- The testing sample is previously unseen by the algorithm and so represents a random sample from the joint probability distribution of x and y.
- The generative probabilistic model is defined through the joint probability density p ( X ^ o, x ^ m, h ) \,.
- If the network structure of the model is a directed acyclic graph, the model represents a factorization of the joint probability of all random variables.
- Moreover, the joint probability distribution of the energy values of two different microscopic configurations of the spins, \ sigma and \ sigma'factorizes:
- Note however that we derived the simplified expression for the conditional distribution above simply by rewriting the expression for the joint probability and removing constant factors.
- RDM can also incorporate probabilistic information, but rejects the view that a single joint probability distribution represents the best description of a deeply uncertain future.
- Be the joint probability density function of the values of the random variables " f " 1 to " f n ".
- Thus, the joint probability of the home team scoring x goals and the away team scoring y goals is a product of the two independent probabilities:
- If the joint probability distribution can be described with local strategies as above, it can be shown that the correlation function always obeys the following CHSH inequality:
- In this case, the odds ratio equals one, and conversely the odds ratio can only equal one if the joint probabilities can be factored in this way.
- Eigenvalues repel as the joint probability density has a zero ( of \ beta th order ) for coinciding eigenvalues \ lambda _ j = \ lambda _ i.
- If " X " and " Y " are independent, their joint probabilities can be expressed in terms of their marginal probabilities and, as follows
- In the present method, joint probabilities of the samples are found by use of a Markov transition matrix method and this has some mathematical synergy with the bottleneck method itself.
- Despite the fact that is non-negative definite and bounded like a standard joint probability distribution, this similarity may be misleading, because different coherent states are not orthogonal.
- Furthermore, P ( X ) and P ( Y ) is the default probability of X and Y respectively, and P ( XY ) is the joint probability of default.
- As mRMR approximates the combinatorial estimation problem with a series of much smaller problems, each of which only involves two variables, it thus uses pairwise joint probabilities which are more robust.
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