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Random forests or random decision forests is an ensemble learning method for classification, regression and other tasks that works by creating a multitude of decision trees during training. For classification tasks, the output of the random forest is the class selected by most trees.
The Birnbaum–Saunders distribution, also known as the fatigue life distribution, is a probability distribution used extensively in reliability applications to model failure times. The chi distribution. The noncentral chi distribution; The chi-squared distribution, which is the sum of the squares of n independent
In a Dominance preemption model of niche apportionment the species colonize random portion between 50 and 100% of the smallest remaining niche, making this model stochastic in nature. A closely related model, the Geometric Series, [ 9 ] is a deterministic version of the Dominance pre-emption model, wherein the percentage of remaining niche ...
In probability theory and statistics, a stochastic order quantifies the concept of one random variable being "bigger" than another. These are usually partial orders, so that one random variable may be neither stochastically greater than, less than, nor equal to another random variable . Many different orders exist, which have different ...
non-parametric hierarchical Bayesian models, such as models based on the Dirichlet process, which allow the number of latent variables to grow as necessary to fit the data, but where individual variables still follow parametric distributions and even the process controlling the rate of growth of latent variables follows a parametric distribution.
In mathematics and computer science, a random tree is a tree or arborescence that is formed by a stochastic process. Types of random trees include: Types of random trees include: Uniform spanning tree , a spanning tree of a given graph in which each different tree is equally likely to be selected
Evolution of forest with p/f=100 960 frames of a forest fire simulation within 38.4 seconds (25 frames per second) In applied mathematics, a forest-fire model is any of a number of dynamical systems displaying self-organized criticality. Note, however, that according to Pruessner et al. (2002, 2004) the forest-fire model does not behave ...
Here is a simple version of the nested sampling algorithm, followed by a description of how it computes the marginal probability density = where is or : Start with N {\displaystyle N} points θ 1 , … , θ N {\displaystyle \theta _{1},\ldots ,\theta _{N}} sampled from prior.