確率・統計:Probability and Statistics

python

Overview of softmax functions and related algorithms and implementation examples

Overview of Softmax Functions A softmax function is a function used to convert a vector of real numbers in...
python

Overview of Dirichlet distribution and related algorithms and implementation examples

Overview of Dirichlet distribution The Dirichlet distribution (Dirichlet distribution) is a type of multiv...
python

Overview of Gelman-Rubin Statistics and Related Algorithms and Examples of Implementations

Overview of Gelman-Rubin Statistics The Gelman-Rubin statistic (or Gelman-Rubin diagnostic, Gelman-Rubin statis...
幾何学:Geometry

Cross-Entropy Loss

Overview of  Cross-Entropy Loss Cross-Entropy Loss (Cross-Entropy Loss) is one of the common loss functions use...
python

Overview of Q-Learning and Examples of Algorithms and Implementations

  Q-Learning Q-Learning (Q-Learning) is a type of reinforcement learning, an algorithm that allows an agent t...
アルゴリズム:Algorithms

Overview, Algorithm and Implementation of Gauss-Hermite Integral

Gaussian-Hermite Integration Gaussian-Hermite Integration is a method of numerical integration, often used for ...
python

Fermi Estimation Statistics and Artificial Intelligence Technology

Fermi Estimation with Statistics Fermi estimation (Fermi estimation) is a method for making rough estimate...
アルゴリズム:Algorithms

Overview of Kullback-Leibler variational estimation and various algorithms and implementations

Kullback-Leibler Variational Estimation Kullback-Leibler Variational Estimation (Kullback-Leibler Variatio...
アルゴリズム:Algorithms

An overview of maximum likelihood estimation and its algorithm and implementation

Maximum Likelihood Estimation Maximum Likelihood Estimation (MLE) will be one of the estimation methods used in...
アルゴリズム:Algorithms

Overview of Bayesian Structural Time Series Models and Examples of Application and Implementation

Bayesian Structural Time Series Models Bayesian Structural Time Series Model (BSTS) is a type of statisti...
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