ベイズ推定

Clojure

Chinese resturant process (CRP) using Clojure and its application to mixed Gaussian distributions

Application to the Chinese resturant process (CRP) and mixed Gaussian distribution using Clojure for probabilistic generative models used in digital transformation, artificial intelligence, and machine learning tasks
アルゴリズム:Algorithms

Protected: Equivalence of neural networks (deep learning) and Gaussian processes

On the equivalence of Gaussian processes and neural networks, an applied model of stochastic generative models used in digital transformation, artificial intelligence, and machine learning tasks from Neal's paper
アルゴリズム:Algorithms

Protected: Application of Variational Bayesian Algorithm to Latent Dirichlet Models

Application of the variational Bayesian algorithm, a computational method for stochastic generative models utilized in digital transformation, artificial intelligence , and machine learning tasks, to latent Dirichlet models
アルゴリズム:Algorithms

Protected: Maximum Propagation Method for Calculating MAP Assignments in Graphical Models

Estimating the maximized state of probability values (MAP assignment) with the maximum propagation method in probabilistic generative models used in digital transformation, artificial intelligence, and machine learningtasks (TRW maximum propagation method, STA condition, maximum propagation method on a factor graph with cycles, maximum propagation on a tree graph, MAP estimation by message propagation)
アルゴリズム:Algorithms

Protected: Foundations of Measure Theory for Nonparametric Bayesian Theory

Foundations of measure theory for nonparametric Bayesian theory (independence of random measures, monotone convergence theorem in Laplace functionals, propositions valid with probability 1, Laplace transform of probability distribution, expectation computation by probability distribution, probability distribution, monotone convergence theorem, approximation theorem by single functions, single functions, measurable functions using Borel set families, Borel sets, σ-finite measures, σ-additive families, Lebesgue measures, Lebesgue integrals)
アルゴリズム:Algorithms

Protected: Calculation of Gaussian processes (1) Calculation by the auxiliary variable method

Approximate computation of Gaussian process models, an application of stochastic generative models utilized in digital transformation, artificial intelligence, and machine learning tasks, using the partial data method and the auxiliary variable method
Clojure

Probabilistic Programming with Clojure

Probabilistic programming with Clojure.Anglican utilized for digital transformation, artificial intelligence, and machine learning tasks gorilla-repl, Stan, BUGS, Picture, Turing, Gen, PyStan PyMC, pomegranater, Lea, bayesloop, Edward, Tensorflow Probability, Pyro, BLOG, Dyna, ProbLog, Church, Anglican, Distributions
Symbolic Logic

Modeling that combines probability and logic (2) PLL (Probabilistic Logical Learning)

Fusion modeling of probability and logic Probabilistic Logical Learning, ILP, PRISM used for digital transformation, artificial intelligence, and machine learning tasks.
アルゴリズム:Algorithms

Protected: Replica Exchange Monte Carlo and Multicanonical Methods

On replica exchange Monte Carlo and multi-canonical methods, which are algorithms to avoid falling into local solutions in Markov chain Monte Carlo methods used for digital transformation, artificial intelligence, and machine learning tasks.
アルゴリズム:Algorithms

Protected: Application of Variational Bayesian Algorithm to Mixed Gaussian Distribution Models

Application of variational Bayesian algorithms to mixed Gaussian distribution models for the computation of stochastic generative models utilized in digital transformation, artificial intelligence, and machine learning tasks (Dirichlet distribution, isotropic Gaussian distribution, free energy calculation)
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