marginal probability वाक्य
उदाहरण वाक्य
मोबाइल
- The corresponding marginal probability density for the off-diagonal element is therefore the variance-gamma distribution
- The MC quantifies the model representation size in terms of number of bits required to store all the marginal probabilities
- We know the marginal probabilities of the variables, but we do not know whether they are correlated or not.
- Similarly, the marginal probability that P ( H = Not Hit ) is the sum of the H = Not Hit row.
- MRFs are completely characterized by their prior probability distributions, marginal probability distributions, cliques, smoothing constraint as well as criterion for updating values.
- This, again, reflects the mathematical relationship between key concepts in Bayesian inference ( namely marginal probability, conditional probability, and posterior probability ).
- This allows for the notion of a reduced or marginal probability on both Alice and Bob's measurements, and is formalised by the conditions:
- If " X " and " Y " are independent, their joint probabilities can be expressed in terms of their marginal probabilities and, as follows
- In the general case we may have a large number of model parameters, and an inspection of the marginal probability densities of interest may be impractical, or even useless.
- The odds ratio is a function of the cell probabilities, and conversely, the cell probabilities can be recovered given knowledge of the odds ratio and the marginal probabilities and.
- Then, the conditional probability table of x _ 1 provides the marginal probability values for P ( x _ 1 \ mid x _ 2, x _ 3 ).
- Here is a simple version of the nested sampling algorithm, followed by a description of how it computes the marginal probability density Z = P ( D | M ) where M is M1 or M2:
- So in this case the answer for the marginal probability can be found by summing P ( H, L ) for all possible values of L, with each value of L weighted by its probability of occurring.
- The summation can be interpreted as a weighted average, and consequently the marginal probability, \ Pr ( A ), is sometimes called " average probability "; " overall probability " is sometimes used in less formal writings.
- The marginal probability P ( H = Hit ) is the sum along the H = Hit row of this joint distribution table, as this is the probability of being hit when the lights are red OR yellow OR green.
- In statistics, the "'conditional probability table ( CPT ) "'is defined for a set of discrete ( not independent ) random variables to demonstrate marginal probability of a single variable with respect to the others.
- (where each " f i " is not necessarily a density ) then the " n " variables in the set are all independent from each other, and the marginal probability density function of each of them is given by
- In fact, the result is only affected by the relative marginal probabilities of winning \ operatorname { P } [ E _ 1 ] and \ operatorname { P } [ E _ 2 ]; in particular, the probability of a draw is irrelevant.
- :So that 1 minus the ratio of the winning probabilities on first and second serve ( which is less than one, from the first constraint ) is less than the marginal probability of getting your second serve first in, compared to the first serve.
- For an M / G / 2 queue ( the model with two servers ) the problem of determining marginal probabilities can be reduced to solving a pair of integral equations or the Laplace transform of the distribution when the service time distribution is a mixture of exponential distributions.
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