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  2. Joint probability distribution - Wikipedia

    en.wikipedia.org/wiki/Joint_probability_distribution

    If the points in the joint probability distribution of X and Y that receive positive probability tend to fall along a line of positive (or negative) slope, ρ XY is near +1 (or −1). If ρ XY equals +1 or −1, it can be shown that the points in the joint probability distribution that receive positive probability fall exactly along a straight ...

  3. Chow–Liu tree - Wikipedia

    en.wikipedia.org/wiki/Chow–Liu_tree

    In probability theory and statistics Chow–Liu tree is an efficient method for constructing a second-order product approximation of a joint probability distribution, first described in a paper by Chow & Liu (1968). The goals of such a decomposition, as with such Bayesian networks in general, may be either data compression or inference.

  4. Collectively exhaustive events - Wikipedia

    en.wikipedia.org/wiki/Collectively_exhaustive_events

    Another example of events being collectively exhaustive and mutually exclusive at same time are, event "even" (2,4 or 6) and event "odd" (1,3 or 5) in a random experiment of rolling a six-sided die. These both events are mutually exclusive because even and odd outcome can never occur at same time.

  5. Stochastic process - Wikipedia

    en.wikipedia.org/wiki/Stochastic_process

    More precisely, a real-valued continuous-time stochastic process with a probability space (,,) is separable if its index set has a dense countable subset and there is a set of probability zero, so () =, such that for every open set and every closed set = (,), the two events {} and {} differ from each other at most on a subset of .

  6. Chapman–Kolmogorov equation - Wikipedia

    en.wikipedia.org/wiki/Chapman–Kolmogorov_equation

    where P(t) is the transition matrix of jump t, i.e., P(t) is the matrix such that entry (i,j) contains the probability of the chain moving from state i to state j in t steps. As a corollary, it follows that to calculate the transition matrix of jump t , it is sufficient to raise the transition matrix of jump one to the power of t , that is

  7. Exchangeable random variables - Wikipedia

    en.wikipedia.org/wiki/Exchangeable_random_variables

    A sequence of random variables that are i.i.d, conditional on some underlying distributional form, is exchangeable. This follows directly from the structure of the joint probability distribution generated by the i.i.d. form. Mixtures of exchangeable sequences (in particular, sequences of i.i.d. variables) are exchangeable.

  8. Likelihood function - Wikipedia

    en.wikipedia.org/wiki/Likelihood_function

    Download as PDF; Printable version; ... It is constructed from the joint probability distribution of the random variable that ... One example occurs in 2×2 tables, ...

  9. Chain rule (probability) - Wikipedia

    en.wikipedia.org/wiki/Chain_rule_(probability)

    This rule allows one to express a joint probability in terms of only conditional probabilities. [4] The rule is notably used in the context of discrete stochastic processes and in applications, e.g. the study of Bayesian networks, which describe a probability distribution in terms of conditional probabilities.