ベイズ推定

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

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: Machine Translation Today and Tomorrow – Different Machine Learning Approaches for Natural Language

Present and future of machine translation for digital transformation, artificial intelligence, and machine learning tasks - Different machine learning approaches for natural language machine translation based on attentional neural nets, machine translation based on encoding and decoding, recurrent neural nets, machine translation based on neural nets and neural models, neural net-based translation, tree-to-strong translation, pre-ordering, parse trees and parsing, word mapping, phrase-based translation

2022.10.15

アルゴリズム:Algorithmsグラフ理論ベイズ推定最適化: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自然言語処理:Natural Language Processing

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

Protected: Nonparametric Bayes from the viewpoint of point processes – Poisson and gamma processes

Nonparametric Bayes from the viewpoint of point processes as an application of stochastic generative models used in digital transformation, artificial intelligence, and machine learning tasks - Poisson and gamma processes additive processes, Poisson random measures, gamma random measures, discreteness, Laplace functional, point processes

2022.10.11

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: Computational Methods for Gaussian Processes(2)Variational Bayesian Method and Stochastic Gradient Method

Calculations using variational Bayesian and stochastic gradient methods for Gaussian process models, an application of stochastic generative models utilized in digital transformation, artificial intelligence, and machine learning tasks Kullback-Leibler information content, Jensen inequality, evidence lower bound function, mini-batch method, evidence lower bound, variational posterior distribution, evidence variational lower bound

2022.10.10

アルゴリズム:Algorithmsグラフ理論スパースモデリング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

Implementation of hidden Markov model with Viterbi algorithm and stochastic generative model using Clojure

Implementation of hidden Markov models with Viterbi algorithm and probabilistic generative models using Clojure for digital transformation, artificial intelligence, and machine learning tasks

2022.10.06

Clojure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