Modeling Gaussian and non-Gaussian worlds
Gaussian world
The probabilistic approach to machine learning, described in “Probabilistic Approaches to M...
確率・統計:Probability and Statistics
Python and Machine Learning (1) Mathematics and Basic Algorithms
Python and Machine Learning
Overview
Python will be a general-purpose programming language with many e...
Overview of Bayesian Multivariate Statistical Modeling and Examples of Algorithms and Implementations
Overview of Bayesian Multivariate Statistical Modeling
Bayesian multivariate statistical modeling is a met...
Overview of Stochastic Gradient Langevin Dynamics (SGLD) and examples of algorithms and implementations
Stochastic Gradient Langevin Dynamics（SGLD）
Stochastic Gradient Langevin Dynamics (SGLD) is a stochastic optimi...
KL divergence constraint
KL divergence constraint
The KL divergence (Kullback-Leibler Divergence) is an asymmetric measure of simil...
Epistemic uncertainty and AI complementation
epistemic uncertainty
Epistemic Uncertainty refers to uncertainty arising from a lack or incompleteness of...
Aleatory uncertainty and AI-based solutions
Aleatory Uncertainty
Aleatory Uncertainty will mainly refer to uncertainty caused by natural phenomena and...
Philosophical perspectives on probability and AI solutions to uncertainty
Philosophical perspectives on probability
The concept of probability has different perspectives. The diffe...
Overview of Stochastic Gradient Descent (SGD), its algorithms and examples of implementation
Overview of Stochastic Gradient Descent, SGD
Stochastic Gradient Descent (SGD) is one of the optimization algori...
Overview of the Dirichlet Process Mixture Model (DPMM), its algorithm and examples of implementation
Dirichlet Process Mixture Model (DPMM) Overview
The Dirichlet Process Mixture Model (DPMM) is one of the v...