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  2. Log-normal distribution - Wikipedia

    en.wikipedia.org/wiki/Log-normal_distribution

    A log-normal process is the statistical realization of the multiplicative product of many independent random variables, each of which is positive. This is justified by considering the central limit theorem in the log domain (sometimes called Gibrat's law ).

  3. Gaussian process - Wikipedia

    en.wikipedia.org/wiki/Gaussian_process

    In probability theory and statistics, a Gaussian process is a stochastic process (a collection of random variables indexed by time or space), such that every finite collection of those random variables has a multivariate normal distribution.

  4. Independent and identically distributed random variables

    en.wikipedia.org/wiki/Independent_and...

    Machine learning (ML) involves learning statistical relationships within data. To train ML models effectively, it is crucial to use data that is broadly generalizable. If the training data is insufficiently representative of the task, the model's performance on new, unseen data may be poor.

  5. Logistic regression - Wikipedia

    en.wikipedia.org/wiki/Logistic_regression

    Logistic regression is used in various fields, including machine learning, most medical fields, and social sciences. For example, the Trauma and Injury Severity Score (), which is widely used to predict mortality in injured patients, was originally developed by Boyd et al. using logistic regression. [6]

  6. Statistical learning theory - Wikipedia

    en.wikipedia.org/wiki/Statistical_learning_theory

    Supervised learning involves learning from a training set of data. Every point in the training is an input–output pair, where the input maps to an output. The learning problem consists of inferring the function that maps between the input and the output, such that the learned function can be used to predict the output from future input.

  7. Neural network Gaussian process - Wikipedia

    en.wikipedia.org/.../Neural_network_Gaussian_process

    A Neural Network Gaussian Process (NNGP) is a Gaussian process (GP) obtained as the limit of a certain type of sequence of neural networks. Specifically, a wide variety of network architectures converges to a GP in the infinitely wide limit , in the sense of distribution .

  8. Flow-based generative model - Wikipedia

    en.wikipedia.org/wiki/Flow-based_generative_model

    A flow-based generative model is a generative model used in machine learning that explicitly models a probability distribution by leveraging normalizing flow, [1] [2] [3] which is a statistical method using the change-of-variable law of probabilities to transform a simple distribution into a complex one.

  9. Normalization - Wikipedia

    en.wikipedia.org/wiki/Normalization

    URL normalization, process to modify URLs in a consistent manner; Normalization (machine learning), a technique in machine learning to change activation patterns to be on a similar scale. Normalized frequency (digital signal processing), unit of frequency cycles/sample in digital signal processing