Estimation

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)

2022.09.28

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: Non-patometric Bayes and clustering (2) Stochastic model of partitioning and Dirichlet processes

Clustering using nonparametric Bayes, one of the applications of probabilistic generative models utilized in digital transformation, artificial intelligence, and machine learningtasks (Chinese restaurant process and Dirichlet process and concentration parameter estimation, bar-folding process)

2022.08.16

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

Protected: Estimating Bunt Effects Using Propensity Scores

Estimating baseball bunt effects using propensity scores as an application of causal inference for digital transformation, artificial intelligence, and machine learning tasks.

2022.04.07

Symbolic Logicグラフ理論推論技術:inference Technology数理論理学最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics

Protected: Basics of Statistical Causal Effects (1)Definition of Causal Effects Based on the Rubin Effect Model

Definition of causal effects and estimation of statistical causal effects (ATT, ATU, ATE) based on the Rubin effect model used for digital transformation, artificial intelligence and machine learning tasks

2022.03.29

Symbolic Logicグラフ理論推論技術:inference Technology数理論理学最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics

Protected: LiNGAM (3)Estimation of LiNGAM model (1)Approach using independent component analysis and regression analysis

Estimation of LiNGAM models using independent component analysis (Hungarian method) and regression analysis (adaptive Lasso) for probabilistic causal search for digital transformation and artificial intelligence task applications

2022.03.09

Symbolic Logicグラフ理論ベイズ推定推論技術:inference Technology最適化:Optimization検索技術:Search Technology機械学習:Machine Learning確率・統計:Probability and Statistics

Protected: Topic models – maximum likelihood estimation, variational Bayesian estimation, estimation by Gibbs sampling

Maximum likelihood, variational Bayesian, and Gibbs sampling estimation of topic models for digital transformation , artificial intelligence , and natural language processing tasks.

2021.12.29

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自然言語処理:Natural Language Processing

Protected: From Averages to Individuality: The Open World of Statistical Modeling (2)hierarchical Bayesian model

From the Statistics of Means to the Statistics of Individuality Estimation with Bayesian Modeling

2021.09.23

機械学習:Machine Learning確率・統計:Probability and Statistics