All Classes
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All Classes Interface Summary Class Summary Class Description ABayesianInferenceAlgorithm AbstractVector An abstract vector class, implementing several common methods for different vector implementations.AffineFunction BayesianInferenceProblem BayesNet BlackBoxGradient Difference quotient based gradient estimation.DenseDoubleVector Dense vector implementation wrapping the MTJ implementation of a dense vector.DiscreteProbabilityDistribution DiscreteProbabilityDistributionPrinter EnumerationBasedBayesianInferenceSolver GradientDescentOptimizer An optimizer based on the gradient descent method [1].IGradientBasedOptimizer Interface for an optimizer that is based on a gradient descent and gets a differentiable function and the derivation of said function to solve an optimization problem.IGradientDescendableFunction This interface represents a function that is differentiable and thus can be used by gradient descent algorithms.IGradientDescentOptimizerConfig IGradientFunction Represents the gradient of a function that is differentiable.IRealFunction ISingleMinimumComputer Interface to compute the place of a function f where f is locally minimalLinearlyScaledFunction PLInferenceProblem PLInferenceProblemEncoder PLMMAlgorithm This is the MM algorithm for Plackett-Luce as described inPLSkillMap RandomGenerator This class serves as a way to obtain a globally synchronized random variable.SparseDoubleVector Sparse vector implementation wrapping the MTJ implementation of a sparse vector.VariableElimination