数理論理学

Symbolic Logic

Protected: Correlation, Causation and Relational Structure (2) Backdoor Criteria

Actual backdoor criteria for narrowing down variables to observe intervention effects in causal inference for digital transformation , artificial intelligence, and machine learning tasks
2022.03.23
Symbolic Logicグラフ理論ベイズ推定推論技術:inference Technology数理論理学最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics
Symbolic Logic

Protected: Discrepancy between correlation (regression coefficient) and causation (intervention effect)

Differences between regression coefficients and intervention effect values for digital transformation, artificial intelligence , and machine learning tasks.
2022.03.22
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関係データ学習
Symbolic Logic

Machine Learning Professional Series “Submodular Optimization and Machine Learning” reading notes

  Machine Learning Professional Series "Submodular Optimization and Machine Learning" reading notes Submodular f...
2022.03.21
Symbolic Logicアルゴリズム:Algorithms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
グラフ理論

Bayesian inference and MCMC open source software

Bayesian inference and MCMC open source software Bayesian statistics means that not only the data, but also th...
2022.03.20
グラフ理論ベイズ推定推論技術:inference Technology数理論理学最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics
Symbolic Logic

Protected: Causal InferenceIntroduction(2)Stratified Analysis and Regression Modeling

Theory and practice of causal inference through analysis by stratified analysis and regression models for statistical causal estimation used in digital transformation , artificial intelligence , and machine learning tasks
2022.03.18
Symbolic Logicグラフ理論ベイズ推定推論技術:inference Technology数理論理学最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics
Symbolic Logic

Protected: Introduction to Causal Inference (1) Confounding Factors and Randomized Experiments

Introduction to statistical causal inference (randomized experiments controlling for confounding factors to distinguish between causality and pseudo-correlation)
2022.03.17
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
グラフ理論

Machine Learning Professional Series “Graphical Models” reading notes

Summary Bayesian estimation can be one of the statistical methods for interpreting data and learning models from...
2022.03.16
グラフ理論ベイズ推定推論技術:inference Technology数理論理学数理論理学:Mathematical logic最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics関係データ学習
グラフ理論

Nonparametric Bayesian and Gaussian Processes

Nonparametric Bayesian and Gaussian Processes Overview Nonparametric Bayes is a method of Bayesian statistics, a...
2022.03.16
グラフ理論ベイズ推定微分積分:Calculus推論技術:inference Technology数理論理学最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics関係データ学習
LISP

Knowledge Information Processing Technologies

Overview of techniques for handling the most important knowledge information in artificial intelligence tasks.
2021.12.19
LISPprologSymbolic Logicエキスパートシステム:expertsystemセマンテックウェブ技術:Semantic web Technologyデータベース技術:DataBase Technology推論技術:inference Technology数理論理学検索技術:Search Technology
推論技術:inference Technology

Answer Set Programming: A Brief History of Logic Programming and ASP

A solution set program as a tool for representing complex knowledge information used in digital transformation and artificial intelligence tasks.
2021.12.19
推論技術:inference Technology数理論理学
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