Bayesian inference

Protected: An example of machine learning by Bayesian inference: inference by Gibbs sampling of a Poisson mixture model

Examples of machine learning with Bayesian inference utilized for digital transformation, artificial intelligence, and machine learning tasks: inference by Gibbs sampling of Poisson mixed models (algorithm, sampling of unobserved variables, Dirichlet distribution, gamma distribution, conditional distribution, categorical distribution, posterior distribution, simultaneous distribution, superparameter, knowledge model, latent variable) categorical distribution, posterior distribution, simultaneous distribution, hyperparameters, knowledge models, data generating processes, latent variables)

2023.01.30

アルゴリズム:Algorithmsグラフ理論ベイズ推定幾何学:Geometry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: Approximate computation of various models in machine learning by Bayesian inference

Approximate computation of various models in machine learning using Bayesian inference for digital transformation, artificial intelligence, and machine learning tasks (structured variational inference, variational inference algorithms, mixture models, conjugate prior, KL divergence, ELBO, evidence lower bound, collapsed Gibbs sampling, blocking Gibbs sampling, approximate inference)

2023.01.23

アルゴリズム:Algorithmsグラフ理論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: Machine Learning with Bayesian Inference – Mixture Models, Data Generation Process and Posterior Distribution

Mixture models and data generation processes and posterior distributions (graphical models, Poisson distribution, Gaussian distribution, Dirichlet distribution, categorical distribution) in machine learning with Bayesian inference used in digital transformation, artificial intelligence, machine learning

2022.12.19

アルゴリズム:Algorithmsグラフ理論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

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: Calculation of marginal probability distributions – Probability Propagation Method

Compute the probability distribution around graphical models in probabilistic generative models used in digital transformation, artificial intelligence , and machine learning tasks, such as Bayesian estimation, using probability propagation methods

2022.07.06

アルゴリズム:Algorithmsグラフ理論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

Shaky Proteins and Old Me: Data Science in the Age of Misfolding

Integration of simulation and machine learning technologies used in digital transformation, artificial intelligence and machine learningtasks; application of simulation and machine learning PCA, RMA, canonical correlation analysis, independent component analysis, Bayesian inference, hidden Markov models) to protein functional analysis (misfolding, etc.

2022.06.08

IOT技術:IOT TechnologyStream Data Processingシミュレーション地理空間情報処理微分積分:Calculus推論技術:inference Technology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

Protected: Bayesian Deep Learning – Introduction

Overview of Bayesian deep models, an evolution of deep learning and probabilistic generative models.

2021.05.25

アルゴリズム:Algorithms機械学習:Machine Learning深層学習:Deep Learning確率・統計:Probability and Statistics