微分積分:Calculus

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

Protected: Structural learning of graphical models

On learning graph structures from data in Bayesian networks and Markov probability fields (Max-Min Hill Climbing (MMHC), Chow-Liu's algorithm, maximizing the score function, PC (Peter Spirtes and Clark Clymoir) Algorithm, GS (Grow-Shrink) algorithm, SGS (Spietes Glymour and Scheines) algorithm, sparse regularization, independence condition)

2022.10.26

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: Nonparametric Bayes from the viewpoint of point processes – Normalized gamma processes, Dirichlet processes and complete random measures

Nonparametric Bayes from the viewpoint of point processes utilized in digital transformation, artificial intelligence and machine learning tasks - Normalized gamma and Dirichlet processes and complete random measures Poisson processes, Livy measures, gamma random measures, beta random measures, Levy-Ito decomposition

2022.10.25

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集合論:Set theory

Protected: Calculation method for Gaussian processes based on a lattice arrangement of auxiliary points

Gaussian process method calculations based on lattice-like auxiliary point arrangements in Gaussian process models Kronecker method, Teblitz method, local kernel interpolation, KISS-GP method, an application of stochastic generative models used in digital transformation, artificial intelligence, machine learning

2022.10.24

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

State Space Model with Clojure: Implementation of Kalman Filter

State space models using Clojure for digital transformation (DX), artificial intelligence (AI), and machine learning (ML) tasks: implementation of Kalman filter

2022.10.19

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

Protected: Analysis of time series data using Clojure

Analysis of time series data such as AR, MA, ARMA, etc. using Clojure for digital transformation, artificial intelligence machine learning tasks ACF, PACF, Partial Autocorrelation, Durbin-Levinson algorithm, autocovariance, moving average models, autocorrelation models, hybrid, random walk, discrete-time models

2022.10.18

Clojureアルゴリズム:Algorithms微分積分:Calculus時系列データ解析最適化: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

Analysis in R and Clojure using Stan for Markov Chain Monte Carlo (MCMC) models

Implementation using R and Clojure of Stan, a computational tool using MCMC for Bayesian estimation used in digital transformation, artificial intelligence, and machine learning tasks.

2022.10.17

ClojureR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

Protected: Overview of model-based approach to reinforcement learning and its implementation in python

Overview of reinforcement learning with model-based approaches used for digital transformation, artificial intelligence, and machine learning tasks and its implementation in python Bellman Equation, Value Iteration, Policy Iteration

2022.10.14

pythonアルゴリズム:Algorithmsオンライン学習強化学習微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics線形代数:Linear Algebra

Protected: Bayesian Learning and Conjugacy

Conjugacy of various probability functions (Gaussian, Bernoulli, Poisson, Dirichlet, and Gamma distributions) and prior distributions for Bayesian learning calculations in stochastic generative models utilized in digital transformation, artificial intelligence, and machine learning tasks

2022.10.13

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

Protected: A linear summation method and message propagation algorithm for MAP estimation of discrete-state graphical models

MAP estimation using linear programming in a graphical model of discrete states in a stochastic generative model (max-sum diffusion (MSD) algorithm, Generalized MPLP, MPLP algorithm, dual solution of the relaxation problem, dual decomposition, solution by message propagation, separation algorithm, cycle inequality, MAP estimation problem formulated as a linear programming problem)

2022.10.12

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