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  2. Nested sampling algorithm - Wikipedia

    en.wikipedia.org/wiki/Nested_sampling_algorithm

    A NestedSampler is part of the Python toolbox BayesicFitting [9] for generic model fitting and evidence calculation. It is available on GitHub. An implementation in C++, named DIAMONDS, is on GitHub. A highly modular Python parallel example for statistical physics and condensed matter physics uses is on GitHub.

  3. Oversampling and undersampling in data analysis - Wikipedia

    en.wikipedia.org/wiki/Oversampling_and_under...

    For example, the individual components of a differential white blood cell count must all add up to 100, because each is a percentage of the total. Data that is embedded in narrative text (e.g., interview transcripts) must be manually coded into discrete variables that a statistical or machine-learning package can deal with.

  4. Self-organizing map - Wikipedia

    en.wikipedia.org/wiki/Self-organizing_map

    A self-organizing map (SOM) or self-organizing feature map (SOFM) is an unsupervised machine learning technique used to produce a low-dimensional (typically two-dimensional) representation of a higher-dimensional data set while preserving the topological structure of the data.

  5. scikit-learn - Wikipedia

    en.wikipedia.org/wiki/Scikit-learn

    scikit-learn (formerly scikits.learn and also known as sklearn) is a free and open-source machine learning library for the Python programming language. [3] It features various classification, regression and clustering algorithms including support-vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific ...

  6. Diffusion model - Wikipedia

    en.wikipedia.org/wiki/Diffusion_model

    In machine learning, diffusion models, also known as diffusion probabilistic models or score-based generative models, are a class of latent variable generative models. A diffusion model consists of three major components: the forward process, the reverse process, and the sampling procedure. [1]

  7. Variational autoencoder - Wikipedia

    en.wikipedia.org/wiki/Variational_autoencoder

    In machine learning, a variational autoencoder (VAE) is an artificial neural network architecture introduced by Diederik P. Kingma and Max Welling. [1] It is part of the families of probabilistic graphical models and variational Bayesian methods.

  8. Orange (software) - Wikipedia

    en.wikipedia.org/wiki/Orange_(software)

    Orange is an open-source software package released under GPL and hosted on GitHub.Versions up to 3.0 include core components in C++ with wrappers in Python.From version 3.0 onwards, Orange uses common Python open-source libraries for scientific computing, such as numpy, scipy and scikit-learn, while its graphical user interface operates within the cross-platform Qt framework.

  9. Hamiltonian Monte Carlo - Wikipedia

    en.wikipedia.org/wiki/Hamiltonian_Monte_Carlo

    For example, in simulations at finite temperature the factor (with the Boltzmann constant) is directly absorbed into and . The algorithm requires a positive integer for number of leapfrog steps L {\displaystyle L} and a positive number for the step size Δ t {\displaystyle \Delta t} .