ベイズ推定

Protected: An example of machine learning by Bayesian inference: inference by Gibbs sampling of a Poisson mixture model

Examples of machine learning with Bayesian inference utilized for digital transformation, artificial intelligence, and machine learning tasks: inference by Gibbs sampling of Poisson mixed models (algorithm, sampling of unobserved variables, Dirichlet distribution, gamma distribution, conditional distribution, categorical distribution, posterior distribution, simultaneous distribution, superparameter, knowledge model, latent variable) categorical distribution, posterior distribution, simultaneous distribution, hyperparameters, knowledge models, data generating processes, latent variables)

2023.01.30

アルゴリズム:Algorithmsグラフ理論ベイズ推定幾何学:Geometry微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics線形代数:Linear Algebra

Protected: Approximate computation of various models in machine learning by Bayesian inference

Approximate computation of various models in machine learning using Bayesian inference for digital transformation, artificial intelligence, and machine learning tasks (structured variational inference, variational inference algorithms, mixture models, conjugate prior, KL divergence, ELBO, evidence lower bound, collapsed Gibbs sampling, blocking Gibbs sampling, approximate inference)

2023.01.23

アルゴリズム:Algorithmsグラフ理論ベイズ推定微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics線形代数:Linear Algebra集合論:Set theory

Protected: Big Data and Bayesian Learning – The Importance of Small Data Learning

Big Data and Bayesian Learning for Digital Transformation (DX), Artificial Intelligence (AI), and Machine Learning (ML) Tasks - Importance of Small Data Learning

2023.01.07

アルゴリズム:Algorithmsグラフ理論ベイズ推定幾何学:Geometry微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics

Protected: Machine Learning with Bayesian Inference – Mixture Models, Data Generation Process and Posterior Distribution

Mixture models and data generation processes and posterior distributions (graphical models, Poisson distribution, Gaussian distribution, Dirichlet distribution, categorical distribution) in machine learning with Bayesian inference used in digital transformation, artificial intelligence, machine learning

2022.12.19

アルゴリズム:Algorithmsグラフ理論ベイズ推定微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics線形代数:Linear Algebra集合論:Set theory

Protected: Unsupervised Learning with Gaussian Processes (2) Extension of Gaussian Process Latent Variable Model

Extension of Gaussian process latent variable models as unsupervised learning by Gaussian processes, an application of stochastic generative models utilized in digital transformation, artificial intelligence, and machine learningtasks ,infinite warp mixture models, Gaussian process dynamics models, Poisson point processes, log Gaussian Cox processes, latent Gaussian processes, elliptic slice sampling

2022.12.05

アルゴリズム:Algorithmsグラフ理論ベイズ推定微分積分:Calculus推論技術:inference Technology最適化:Optimization機械学習:Machine Learning深層学習:Deep Learning確率・統計:Probability and Statistics線形代数:Linear Algebra

Protected: Unsupervised Learning with Gaussian Processes (1)Overview and Algorithm of Gaussian Process Latent Variable Models

Overview and algorithms of unsupervised learning using Gaussian Process Latent Variable Models GPLVM, an application of probabilistic generative models used in digital transformation, artificial intelligence, and machine learning, Bayesian Gaussian Process Latent Variable Models ,Bayesian GPLVM

2022.11.21

アルゴリズム:Algorithmsベイズ推定微分積分:Calculus推論技術:inference Technology数理論理学:Mathematical logic最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics線形代数:Linear Algebra

Protected: Information Geometry of Positive Definite Matrices (1) Introduction of dual geometric structure

Introduction of dual geometric structures as information geometry for positive definite matrices utilized in digital transformation, artificial intelligence, and machine learning tasks (Riemannian metric, tangent vector space, semi-positive definite programming problem, self-equilibrium, Levi-Civita connection, Riemannian geometry, geodesics, Euclidean geometry, ∇-geodesics, tangent vector, tensor quantity, dual flatness, positive definite matrix set)

2022.11.12

グラフ理論スパースモデリングベイズ推定数理論理学最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics線形代数:Linear Algebra集合論:Set theory

Protected: Spatial statistics of Gaussian processes, with application to Bayesian optimization

Spatial statistics of Gaussian processes as an application of stochastic generative models used in digital transformation, artificial intelligence, and machine learning tasks, and tools ARD, Matern kernelsfor Bayesian optimization GPyOpt and GPFlow and GPyTorch

2022.11.07

アルゴリズム:Algorithmsグラフ理論ベイズ推定微分積分:Calculus推論技術:inference Technology最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics線形代数:Linear Algebra

Integration of logic and rules with probability/machine learning

Integration of logic and rules with machine learning (inductive logic programming, statistical relational learning, knowledge-based model building, Bayesian nets, probabilistic logic learning, hidden Markov models) used for digital transformation, artificial intelligence, and machine learning tasks.

2022.10.30

Symbolic Logicアルゴリズム:Algorithmsエキスパートシステム:expertsystemグラフ理論セマンテックウェブ技術:Semantic web Technologyベイズ推定微分積分:Calculus推論技術:inference Technology数理論理学:Mathematical logic最適化:Optimization機械学習:Machine Learning深層学習:Deep Learning確率・統計:Probability and Statistics関係データ学習

Protected: Calculation of marginal likelihood, posterior mean, posterior covariance, and predictive distribution using variational Bayesian methods

Methods for computing marginal likelihoods, posterior means, posterior covariances, and predictive distributions in variational Bayesian methods for digital transformation, artificial intelligence , and machine learning tasks James Stein estimator, maximum likelihood estimation, empirical Bayes estimator, Bayesian free energy, hyperparameters, automatic relevance determination, linear regression models, stochastic complexity, log marginal likelihood empirical Bayesian learning, multinomial distribution models, posterior means, linear regression models

2022.10.27

アルゴリズム:Algorithmsベイズ推定微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics線形代数:Linear Algebra