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2. Mixture model - Wikipedia

   en.wikipedia.org/wiki/Mixture_model

   A typical finite-dimensional mixture model is a hierarchical model consisting of the following components: . N random variables that are observed, each distributed according to a mixture of K components, with the components belonging to the same parametric family of distributions (e.g., all normal, all Zipfian, etc.) but with different parameters

3. EM algorithm and GMM model - Wikipedia

   en.wikipedia.org/wiki/EM_Algorithm_And_GMM_Model

   The EM algorithm consists of two steps: the E-step and the M-step. Firstly, the model parameters and the () can be randomly initialized. In the E-step, the algorithm tries to guess the value of () based on the parameters, while in the M-step, the algorithm updates the value of the model parameters based on the guess of () of the E-step.

4. File:Parameter estimation process infinite Gaussian mixture ...

   en.wikipedia.org/wiki/File:Parameter_estimation...

   Histograms for one-dimensional datapoints belonging to clusters detected by an infinite Gaussian mixture model. During the parameter estimation based on Gibbs sampling , new clusters are created and grow on the data. The legend shows the cluster colours and the number of datapoints assigned to each cluster.

5. Model-based clustering - Wikipedia

   en.wikipedia.org/wiki/Model-based_clustering

   Model-based clustering [1] based on a statistical model for the data, usually a mixture model. This has several advantages, including a principled statistical basis for clustering, and ways to choose the number of clusters, to choose the best clustering model, to assess the uncertainty of the clustering, and to identify outliers that do not ...

6. Normal distribution - Wikipedia

   en.wikipedia.org/wiki/Normal_distribution

   In probability theory and statistics, a normal distribution or Gaussian distribution is a type of continuous probability distribution for a real-valued random variable. The general form of its probability density function is [ 2 ] [ 3 ] f ( x ) = 1 2 π σ 2 e − ( x − μ ) 2 2 σ 2 . {\displaystyle f(x)={\frac {1}{\sqrt {2\pi \sigma ^{2 ...

7. Expectation–maximization algorithm - Wikipedia

   en.wikipedia.org/wiki/Expectation–maximization...

   A Gentle Tutorial of the EM Algorithm and its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models (Technical Report TR-97-021). International Computer Science Institute. includes a simplified derivation of the EM equations for Gaussian Mixtures and Gaussian Mixture Hidden Markov Models.

8. Roblox - Wikipedia

   en.wikipedia.org/wiki/Roblox

   Roblox is an online game platform and game creation system built around user-generated content and games, [1] [2] officially referred to as "experiences". [3] Games can be created by any user through the platform's game engine, Roblox Studio, [4] and then shared to and played by other players. [1]

9. Subspace Gaussian mixture model - Wikipedia

   en.wikipedia.org/wiki/Subspace_Gaussian_mixture...

   Subspace Gaussian mixture model (SGMM) is an acoustic modeling approach in which all phonetic states share a common Gaussian mixture model structure, and the means and mixture weights vary in a subspace of the total parameter space.