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Example implementations demonstrating the nested sampling algorithm are publicly available for download, written in several programming languages. Simple examples in C, R, or Python are on John Skilling's website. A Haskell port of the above simple codes is on Hackage.
Undersampling with ensemble learning. A recent study shows that the combination of Undersampling with ensemble learning can achieve better results, see IFME: information filtering by multiple examples with under-sampling in a digital library environment. [10]
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.
In machine learning, the radial basis function kernel, or RBF kernel, is a popular kernel function used in various kernelized learning algorithms. In particular, it is commonly used in support vector machine classification. [1]
The No U-Turn Sampler (NUTS) [5] is an extension by controlling the number of steps automatically. Tuning L {\displaystyle L} is critical. For example, in the one dimensional N ( 0 , 1 / k ) {\displaystyle {\text{N}}(0,1/{\sqrt {k}})} case, the potential is U ( x ) = k x 2 / 2 {\displaystyle U(x)=kx^{2}/2} which corresponds to the potential of ...
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]
In statistics, Markov chain Monte Carlo (MCMC) is a class of algorithms used to draw samples from a probability distribution.Given a probability distribution, one can construct a Markov chain whose elements' distribution approximates it – that is, the Markov chain's equilibrium distribution matches the target distribution.
A blocked Gibbs sampler groups two or more variables together and samples from their joint distribution conditioned on all other variables, rather than sampling from each one individually. For example, in a hidden Markov model , a blocked Gibbs sampler might sample from all the latent variables making up the Markov chain in one go, using the ...