Live briefing
Everything about Conditional independence
Probability theory concept
In probability theory, conditional independence describes situations in which an observation is irrelevant or redundant when evaluating the certainty of a hypothesis. It is the opposite of conditional dependence. Conditional independence is usually formulated in terms of conditional probability, as a special case where the probability of the hypothesis given the uninformative observation is equal to the probability without. If is the hypothesis, and and are observations, conditional independence can be stated as an equality: Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Conditional independenceA 5-minute orientation
- Skim a recent paper: Regression-based conditional independence test with adaptive kernelsInfluential work since 2022
- Pick up a classic book: Africa must uniteThe most-reprinted book on the subject
- Read the landmark paper: Conditional Independence in Statistical TheoryCited 943 times
- Go sideways: Independence (probability theory)A closely related topic
- Go sideways: Cumulative distribution functionA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🏆 Landmark papers (most cited)
Conditional Independence in Statistical Theory
PARTIAL CORRELATION AND CONDITIONAL CORRELATION AS MEASURES OF CONDITIONAL INDEPENDENCE
Conditional possibilities independence and noninteraction
Conditional Sure Independence Screening
Conditional independence, conditional mixing and conditional association
Conditional Independence for Statistical Operations
Defining conditional independence using collapses
Testing conditional independence using maximal nonlinear conditional correlation
🆕 Influential since 2022
Regression-based conditional independence test with adaptive kernels
Local permutation tests for conditional independence
Conditional independence in max-linear Bayesian networks
Undecidability of Network Coding, Conditional Information Inequalities, and Conditional Independence Implication
A Conditional Mutual Information Estimator for Mixed Data and an Associated Conditional Independence Test
📚 Books worth reading
Africa must uniteKwame Nkrumah · 1963
White niggers of AmericaPierre Vallieres · 1967
Les idéologies au QuébecDenis Monière · 1976
The AbkhaziansGeorge Hewitt · 1998- 📕The challenge of AfricaK. A. Busia · 1962
The CIS handbookPatrick Heenan · 1999
Autonomy and disintegration in IndonesiaDamien Kingsbury, Harry Aveling · 2003
Eyes of fireDavid Robie · 1986
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- Independence (probability theory) Independence is a fundamental notion in probability theory, as in statistics and the theory of stochastic processes. Two events are independent, statistically…
- Cumulative distribution function In probability theory and statistics, the cumulative distribution function (CDF) of a real-valued random variable X, or just distribution function of X…
- Conditional expectation In probability theory, the conditional expectation, conditional expected value, or conditional mean of a random variable is its expected value evaluated…
- Marginal distribution In probability theory and statistics, the marginal distribution of a subset of a collection of random variables is the probability distribution of the…
- Probability density function In probability theory, a probability density function (PDF), density function, or simply density of an absolutely continuous random variable, is a function…
- Uncorrelatedness In probability theory and statistics, two real-valued random variables, X, Y, are said to be uncorrelated if their covariance, cov[X,Y]=E[XY]−E[X]E[Y]…
- Multivariate random variable In probability and statistics, a multivariate random variable or random vector is a list or vector of mathematical variables each of whose value is unknown…
- Smn theorem In computability theory the S m n theorem, written also as "smn-theorem" or "s-m-n theorem" (also called the translation lemma, parameter theorem, and…