levels: discrete probabilities, probability density functions, and measure theory. Conditioning leads to a non-random result if the condition is completely...

interpretation of probability. The conditioning event is interpreted as evidence for the conditioned event. That is, P(A) is the probability of A before accounting...

In probability theory, regular conditional probability is a concept that formalizes the notion of conditioning on the outcome of a random variable. The...

conditioning, air conditioning in a vehicle Ice storage air conditioning, air conditioning using ice storage Solar air conditioning, air conditioning...

Independence (probability theory) The Borel–Cantelli lemmas and Kolmogorov's zero–one law Conditional probability Conditioning (probability) Conditional...

In probability theory and statistics, the conditional probability distribution is a probability distribution that describes the probability of an outcome...

In probability theory, the law (or formula) of total probability is a fundamental rule relating marginal probabilities to conditional probabilities. It...

Classical conditioning (also respondent conditioning and Pavlovian conditioning) is a behavioral procedure in which a biologically potent stimulus (e...

gives a mathematical rule for inverting conditional probabilities, allowing one to find the probability of a cause given its effect. For example, if the...

lemma Wick product Conditioning (probability) Conditional expectation Conditional probability distribution Regular conditional probability Disintegration...

Probability is a branch of mathematics and statistics concerning events and numerical descriptions of how likely they are to occur. The probability of...

Operant conditioning, also called instrumental conditioning, is a learning process in which voluntary behaviors are modified by association with the addition...

probability and post-test probability (alternatively spelled pretest and posttest probability) are the probabilities of the presence of a condition (such...

Bayesian probability (/ˈbeɪziən/ BAY-zee-ən or /ˈbeɪʒən/ BAY-zhən) is an interpretation of the concept of probability, in which, instead of frequency or...

Inductive probability attempts to give the probability of future events based on past events. It is the basis for inductive reasoning, and gives the mathematical...

under radical probabilism and called it probability kinematics. Others have named it Jeffrey conditioning. Probability kinematics is not the only sufficient...

In probability theory, a probability space or a probability triple ( Ω , F , P ) {\displaystyle (\Omega ,{\mathcal {F}},P)} is a mathematical construct...

diagnosis by epidemiology aims to estimate the probability of each candidate condition by comparing their probabilities to have occurred in the first place in...

Independence is a fundamental notion in probability theory, as in statistics and the theory of stochastic processes. Two events are independent, statistically...

Y = y ) = 0 {\displaystyle P(Y=y)=0} . Conditioning on a discrete random variable is the same as conditioning on the corresponding event: E ⁡ ( X ∣ Y...

bias, collider bias, or Berkson's fallacy, is a result in conditional probability and statistics which is often found to be counterintuitive, and hence...

The posterior probability is a type of conditional probability that results from updating the prior probability with information summarized by the likelihood...

of information gives us this table of conditional probabilities for y: With more than one conditioning variable, the table would still have one row for...

In probability theory and statistics, the Poisson distribution (/ˈpwɑːsɒn/) is a discrete probability distribution that expresses the probability of a...

In probability theory, a probability distribution is infinitely divisible if it can be expressed as the probability distribution of the sum of an arbitrary...

radical probabilism and the associated heuristic of probability kinematics, also known as Jeffrey conditioning. Born in Boston, Massachusetts, Jeffrey served...

core of almost every modern estimation approach that includes conditioned probabilities, such as sequential estimation, probabilistic machine learning...

In probability theory, there exist several different notions of convergence of sequences of random variables, including convergence in probability, convergence...

In probability theory and statistics, a Markov chain or Markov process is a stochastic process describing a sequence of possible events in which the probability...

In probability theory, an outcome is a possible result of an experiment or trial. Each possible outcome of a particular experiment is unique, and different...