information retrieval, divergence from randomness (DFR), is a generalization of one of the very first models, Harter's 2-Poisson indexing-model. It is one type...

Uncertain inference Language models Divergence-from-randomness model Latent Dirichlet allocation Feature-based retrieval models view documents as vectors...

A simple interpretation of the KL divergence of P from Q is the expected excess surprisal from using Q as a model instead of P when the actual distribution...

organisation in Germany Dihydroflavonol 4-reductase, an enzyme class Divergence-from-randomness model, in information retrieval Dounreay Fast Reactor, Scotland Dual...

class of divergences. When the points are interpreted as probability distributions – notably as either values of the parameter of a parametric model or as...

when applied to data from non-randomized experiments or observational studies, model-based analysis lacks the warrant of randomization. For observational...

as often as 4. In this view, randomness is not haphazardness; it is a measure of uncertainty of an outcome. Randomness applies to concepts of chance...

the KL divergence (a measure of statistical distance between distributions) between the model being fine-tuned and the initial supervised model. By choosing...

object which depends on random events. The term 'random variable' in its mathematical definition refers to neither randomness nor variability but instead...

information retrieval. The method is one of a collection of DFR ("Divergence From Randomness") models, the basic notion being that Bose–Einstein statistics may...

in theoretical computer science: Extractors are able to extract randomness from random sources that have a large min-entropy; merely having a large Shannon...

statistical model is a mathematical model that embodies a set of statistical assumptions concerning the generation of sample data (and similar data from a larger...

information geometry, a divergence is a kind of statistical distance: a binary function which establishes the separation from one probability distribution...

pseudometrics on distributions Kullback–Leibler divergence Rényi divergence Jensen–Shannon divergence Ball divergence Bhattacharyya distance (despite its name...

The directed Kullback–Leibler divergence in nats of e λ {\displaystyle e^{\lambda }} ("approximating" distribution) from e λ 0 {\displaystyle e^{\lambda...

machines, which enhance randomness beyond what manual shuffling can achieve. With the rise of online casinos, digital random number generators (RNGs)...

graph expresses the conditional dependence structure between random variables. Graphical models are commonly used in probability theory, statistics—particularly...

but also natural selection, gene flow, and mutation contribute to this divergence. This potential for relatively rapid changes in the colony's gene frequency...

cards, the Gilbert–Shannon–Reeds model describes the probabilities obtained from a certain mathematical model of randomly cutting and then riffling a deck...

making biased random steps that are a sum of pure randomness (like a Brownian walker) and gradient descent down the potential well. The randomness is necessary:...

attributes or characteristics, known as strata, then followed by simple random sampling from the stratified groups, where each element within the same subgroup...

vector space, and the result has units of nats. The Kullback–Leibler divergence from N 1 ( μ 1 , Σ 1 ) {\displaystyle {\mathcal {N}}_{1}({\boldsymbol {\mu...

physiological effects of treatments from various psychological sources of bias.[citation needed] The randomness in the assignment of participants to...

simple models (e.g. single aileron on an Euler-Bernoulli beam), control reversal speeds can be derived analytically as for torsional divergence. Control...

approximation error with, for example, the Kullback–Leibler divergence, Bregman divergence, and the Hellinger distance. With indefinitely large samples...

randomization procedure. The model for the response is Y i , j = μ + T i + r a n d o m   e r r o r {\displaystyle Y_{i,j}=\mu +T_{i}+\mathrm {random\...

mixture models, where members of the population are sampled at random. Conversely, mixture models can be thought of as compositional models, where the...

Kullback–Leibler divergence. This leads to the intuition that by maximizing the log-likelihood of a model, you are minimizing the KL divergence of your model from the...

Model collapse is a phenomenon where machine learning models gradually degrade due to errors coming from uncurated training on the outputs of another model...

variable Y; A generative model can be used to "generate" random instances (outcomes) of an observation x. A discriminative model is a model of the conditional...