In probability theory and computer science, a log probability is simply a logarithm of a probability. The use of log probabilities means representing...

In probability theory, a log-normal (or lognormal) distribution is a continuous probability distribution of a random variable whose logarithm is normally...

and the log-likelihood is the "weight of evidence". Interpreting negative log-probability as information content or surprisal, the support (log-likelihood)...

terms of log probability, two events are independent if and only if the log probability of the joint event is the sum of the log probability of the individual...

logarithmic scale, as in log probability. Similar to multiplication operations in linear-scale becoming simple additions in log-scale, an addition operation...

A prior probability distribution of an uncertain quantity, often simply called the prior, is its assumed probability distribution before some evidence...

In probability theory and statistics, the binomial distribution with parameters n and p is the discrete probability distribution of the number of successes...

} is the logarithm, which gives 0 surprise when the probability of the event is 1. In fact, log is the only function that satisfies а specific set of...

x)=1-p(x)} is the probability of x {\displaystyle x} not occurring. Then we have the following definition of the log-odds: log-odds ( x ) = log ⁡ ( p ( x )...

with probability P ( d ) = log 10 ⁡ ( d + 1 ) − log 10 ⁡ ( d ) = log 10 ⁡ ( d + 1 d ) = log 10 ⁡ ( 1 + 1 d ) . {\displaystyle P(d)=\log _{10}(d+1)-\log _{10}(d)=\log...

takes value 1 with probability p and value 0 with probability q = 1 − p. The Rademacher distribution, which takes value 1 with probability 1/2 and value −1...

function that converts log-odds to probability is the logistic function, hence the name. The unit of measurement for the log-odds scale is called a logit...

also called the log-odds since it is equal to the logarithm of the odds p 1 − p {\displaystyle {\frac {p}{1-p}}} where p is a probability. Thus, the logit...

In probability and statistics, the log-logistic distribution (known as the Fisk distribution in economics) is a continuous probability distribution for...

In probability theory and statistics, the geometric distribution is either one of two discrete probability distributions: The probability distribution...

In probability theory and statistics, the exponential distribution or negative exponential distribution is the probability distribution of the distance...

In probability theory and statistics, a probability distribution is the mathematical function that gives the probabilities of occurrence of possible outcomes...

ratio (OR) and sample log odds ratio (LOR): The following joint probability distributions contain the population cell probabilities, along with the corresponding...

Q} be probability density functions of p {\displaystyle p} and q {\displaystyle q} with respect to r {\displaystyle r} . Then − ∫ X P ( x ) log ⁡ Q (...

such as decibels (see Decibel § Addition), log probability, or log-likelihoods. The operations on the log semiring can be defined extrinsically by mapping...

{\bigr ]}~.} In terms of log probability, log ⁡ f X ( x   |   θ ) = η ( θ ) ⋅ T ( x ) − A ( θ ) + B ( x ) . {\displaystyle \log f_{X}\!\left(x\ {\big |}\...

In probability theory and statistics, the Poisson distribution (/ˈpwɑːsɒn/; French pronunciation: [pwasɔ̃]) is a discrete probability distribution that...

distance: a measure of how one reference probability distribution P is different from a second probability distribution Q. Mathematically, it is defined...

making a linear regression on either the log–log probability, the log–log cumulative distribution function, or on log-binned data, but these approaches should...

distribution that the energy of a state is proportional to the negative log probability of that state) yields: Δ E i = − k B T ln ⁡ ( p i=off ) − ( − k B T...

In probability theory and statistics, the Gumbel distribution (also known as the type-I generalized extreme value distribution) is used to model the distribution...

an observed value, scoring rules assign such a score to a predicted probability distribution and an observed value. On the other hand, a scoring function...

distribution in log space (e.g. log-probability or log-density) instead. That is, work with h ( x ) = log ⁡ g ( x ) {\displaystyle h\left(x\right)=\log g\left(x\right)}...

\Pr(w_{i}|w_{j}:j\in N+i)} That is, we maximize the log-probability of the corpus. Our probability model is as follows: Given words { w j : j ∈ N + i }...

relational model Probability Probability bounds analysis Probability box Probability density function Probability distribution Probability distribution function...