確率・統計:Probability and Statistics

Machine Learning Professional Series – Nonparametric Bayesian Point Processes and the Mathematics of Statistical Machine Learning Reading Notes

Summary Nonparametric Bayes is a method of Bayesian statistics that allows one to build probability models fr...

2022.03.06

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

Protected: Fundamentals of statistical causal search (3) Causal Markov conditions, faithfulness, PC algorithm, GES algorithm

Causal Markov conditions, fidelity and constraint-based approaches and score-based approaches in the foundations of statistical causal search for digital transformation , artificial intelligence and machine learning tasks.

2022.03.04

Symbolic Logicグラフ理論セマンテックウェブ技術:Semantic web Technology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: Fundamentals of Statistical Causal Search (2) Three Approaches Identifiability

Identifiability of three approaches for the basis of statistical causal search for digital transformation, artificial intelligence, and machine learning tasks (matrix representation of structural equation models and directed acyclic graphs, average causal effects).

2022.03.03

グラフ理論推論技術: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: Fundamentals of statistical causal search (1) Framework of causal search and three approaches to basic problems

A framework for the foundation of statistical causal search for digital transformation , artificial intelligence , and machine learning tasks and three approaches to the basic problem (nonparametric, parametric, and semiparametric approaches).

2022.03.01

推論技術:inference Technology最適化:Optimization確率・統計:Probability and Statistics

Protected: Fundamentals of Statistical Causal Inference (2) – Structural Causal Models and Randomized Experiments

Structural causal models and randomized experiments as a basis for statistical causal inference for digital transformation, artificial intelligence, and machine learning tasks.

2022.02.28

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

Iwanami Data Science Series vol.3 “Causal Theory Reading Causality from Real World Data” Reading Memo

Summary Techniques to examine "causal relationships" that are not "correlations" are "causal inference" and "cau...

2022.02.27

推論技術:inference Technology機械学習:Machine Learning確率・統計:Probability and Statistics

Protected: Fundamentals of statistical causal inference (1) – Definition of causality by counterfactual model and structural equation model

Foundations of Statistical Causal Inference for Digital Transformation , Artificial Intelligence and Machine Learning Tasks: Defining Causality in Counterfactual Models and Structural Equation Models

2022.02.25

推論技術:inference Technology機械学習:Machine Learning確率・統計:Probability and Statistics

Protected: Combinatorial online prediction

Combinatorial, online prediction problems with a set of discrete structures as the decision space for digital transformation , artificial intelligence , and machine learning task utilization.

2022.02.15

オンライン学習幾何学:Geometry強化学習微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics

Protected: Online prediction based on randomness

Randomness-based FPL(Follow the Perturbed Leader) Strategy and Gumbel Distribution for Improving Online Predictive Performance for Digital Transformation , Artificial Intelligence and Machine Learning Tasks

2022.02.14

オンライン学習幾何学:Geometry強化学習微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics

Protected: Online convex optimization (3) exp concavity and ONS

Convex optimization for online prediction for digital transformation , artificial intelligence , and machine learning tasks (the case of exp concavity and ONS).

2022.02.10

オンライン学習幾何学:Geometry強化学習微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics