ベイズ推定

Protected: Comparison of clustering using k-means and Bayesian estimation methods (mixed Gaussian model)

Comparison of k-means and Bayesian estimation (mixed Gaussian model) clustering as probabilistic generative models utilized in digital transformation, artificial intelligence , and machine learning tasks

2022.07.26

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: Overview of Gaussian Processes(5)Generalization of Gaussian Process Regression

Extensions of probabilistic generative models utilized in digital transformation, artificial intelligence, and machine learning tasks and generalizations of the Cauchy distribution of Gaussian processes as robustness collateral, Gaussian process identification models, and Poisson distributions for machine failure, elementary particle decay, etc.

2022.07.25

アルゴリズム:Algorithms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線形代数:Linear Algebra

Protected: Calculation of marginal probability distribution – Kikuchi approximation

Application of graphical models to stochastic generative models for digital transformation, artificial intelligence, and machine learning tasks; calculation of marginal probability distributions in the generalized stochastic propagation method with Kikuchi free energy functions and comparison with Bethe free energy functions and Hasse diagrams

2022.07.21

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: Overview of Bayesian Estimation with Concrete Examples

Calculate the fundamentals of Bayesian estimation (exchangeability, de Finetti's theorem, conjugate prior distribution, posterior distribution, marginal likelihood, etc.) used in probabilistic generative models for digital transformation, artificial intelligence, and machine learning tasks, based on concrete examples (Dirichlet-multinomial distribution model, gamma-gaussian distribution model).

2022.07.20

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: Overview of Gaussian Processes(4)Hyperparameter Estimation and Generalization of Gaussian Process Regression

Hyperparameter estimation using the gradient descent method of Gaussian process regression for stochastic generative models utilized in digital transformation, artificial intelligence, and machine learning tasks (SCG method, L-BFGS method, global solution using MCMC)

2022.07.19

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数理論理学:Mathematical logic最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics線形代数:Linear Algebra

Protected: Computing the Peripheral Probability Distribution 2 – Bethe Approximation

Variational methods using the Bethe approximation to compute marginal probability distributions in probability propagation methods for probability estimation using graphical models utilized in digital transformation, artificial intelligence, and machine learning tasks.

2022.07.13

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

Protected: Overview of Stochastic Generative Models and Learning

Probabilistic generative models used in digital transformation , artificial intelligence and machine learning , overview of graphical models and maximum likelihood methods, MAP estimation, Bayesian estimation and Gibbs sampling.

2022.07.12

アルゴリズム:Algorithms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: Overview of Gaussian Processes(3)Gaussian Process Regression Model

Computation and optimization of regression models and predictive distributions using Gaussian processes, which are dimensionless stochastic generative models used in digital transformation, artificial intelligence, and machine learning tasks

2022.07.11

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数理論理学:Mathematical logic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

Various probability distributions

Overview of various probabilistic models used as approximate models for probabilistic generative models utilized in digital transformation, artificial intelligence, and machine learning tasks (Student's t distribution, Wishart distribution, Gaussian distribution, gamma distribution, inverse gamma distribution, Dirichlet distribution, beta distribution, categorical distribution, Poisson distribution, Bernoulli distribution)

2022.07.05

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