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searching for Conditional probability distribution 22 found (58 total)

alternate case: conditional probability distribution

Regular conditional probability (1,425 words) [view diff] exact match in snippet view article find links to article

conditioning on the outcome of a random variable. The resulting conditional probability distribution is a parametrized family of probability measures called a

Zero-truncated Poisson distribution (809 words) [view diff] exact match in snippet view article find links to article

distribution or the positive Poisson distribution. It is the conditional probability distribution of a Poisson-distributed random variable, given that the

Sunrise problem (861 words) [view diff] exact match in snippet view article find links to article

young-earth creationist reading of the Bible. To find the conditional probability distribution of p given the data, one uses Bayes' theorem, which some

An Essay towards solving a Problem in the Doctrine of Chances (1,453 words) [view diff] exact match in snippet view article find links to article

probable. The question Bayes addressed was: what is the conditional probability distribution of p, given the numbers of successes and failures so far

Sequential equilibrium (353 words) [view diff] exact match in snippet view article find links to article

probability given the strategies; the beliefs should be the conditional probability distribution on the nodes of the information set, given that it is reached

Relational dependency network (785 words) [view diff] exact match in snippet view article find links to article

to some structural or numerical inconsistencies. If the conditional probability distribution estimation method uses feature selection, it is possible

Probability distribution of extreme points of a Wiener stochastic process (3,379 words) [view diff] exact match in snippet view article find links to article

and fluctuations in financial markets. A formula for the conditional probability distribution of the extremum of the Wiener process and a sketch of its

Variable-order Markov model (1,140 words) [view diff] exact match in snippet view article find links to article

or future states. Specifically, the learner generates a conditional probability distribution P ( x i ∣ s ) {\displaystyle P(x_{i}\mid s)} for a symbol

Relay channel (835 words) [view diff] exact match in snippet view article find links to article

{\displaystyle X_{1},X_{2},Y_{1},} and Y {\displaystyle Y} , and a conditional probability distribution p ( y , y 1 | x 1 , x 2 ) {\displaystyle p(y,y_{1}|x_{1}

Supervised learning (3,011 words) [view diff] exact match in snippet view article find links to article

find g {\displaystyle g} . When g {\displaystyle g} is a conditional probability distribution P ( y | x ) {\displaystyle P(y|x)} and the loss function

Rao–Blackwell theorem (2,164 words) [view diff] exact match in snippet view article find links to article

is defined as an observable random variable such that the conditional probability distribution of all observable data X given T(X) does not depend on the

Thomas Bayes (2,094 words) [view diff] exact match in snippet view article find links to article

conditionally independent given the value of R. Then the conditional probability distribution of R, given the values of X1, ..., Xn, is ( n + 1 ) ! S

Law of total cumulance (1,717 words) [view diff] exact match in snippet view article find links to article

probability p and Y = 0 with probability q = 1 − p. Suppose the conditional probability distribution of X given Y is F if Y = 1 and G if Y = 0. Then we have κ

Swendsen–Wang algorithm (2,337 words) [view diff] no match in snippet view article find links to article

These values are assigned according to the following (conditional) probability distribution: P [ b n , m = 0 | σ n ≠ σ m ] = 1 {\displaystyle P\left[b_{n

Posterior predictive distribution (2,510 words) [view diff] exact match in snippet view article find links to article

node directly to all children, and replacing the former conditional probability distribution associated with each child with the corresponding posterior

Information theory (7,088 words) [view diff] exact match in snippet view article find links to article

received during a unit time over our channel. Let p(y|x) be the conditional probability distribution function of Y given X. We will consider p(y|x) to be an inherent

Wiener process (5,875 words) [view diff] exact match in snippet view article find links to article

{\displaystyle W_{t}} , it is possible to calculate the conditional probability distribution of the maximum in interval [ 0 , t ] {\displaystyle [0,t]}

Dirichlet-multinomial distribution (6,934 words) [view diff] exact match in snippet view article find links to article

categorical variables dependent on prior d. Accordingly, the conditional probability distribution can be written as follows: Pr ( z d n = k ∣ Z ( − d n )

Rule of succession (4,780 words) [view diff] exact match in snippet view article find links to article

independent given p. We can use Bayes' theorem to find the conditional probability distribution of p given the data Xi, i = 1, ..., n. For the "prior" (i

Lutz–Kelker bias (2,309 words) [view diff] exact match in snippet view article find links to article

parallax due to errors in measurement, we can write the conditional probability distribution function of measuring a parallax of p o {\displaystyle p_{o}}

Differential item functioning (5,511 words) [view diff] exact match in snippet view article find links to article

case, the absence of DIF is determined by the fact that the conditional probability distribution of Y is not dependent on group membership. To illustrate

Drift plus penalty (7,204 words) [view diff] exact match in snippet view article find links to article

random event ω ∈ Ω {\displaystyle \omega \in \Omega } , a conditional probability distribution for selecting a control action α ( t ) ∈ A {\displaystyle