probability theory, a Markov kernel (also known as a stochastic kernel or probability kernel) is a map that in the general theory of Markov processes plays...

{\mathcal {F}},\mathbb {P} )} is called a time homogeneous Markov chain with Markov kernel p {\displaystyle p} and start distribution μ {\displaystyle...

{\displaystyle \Omega } . The discriminator's strategy set is the set of Markov kernels μ D : Ω → P [ 0 , 1 ] {\displaystyle \mu _{D}:\Omega \to {\mathcal {P}}[0...

measures or stochastic processes. The most important example of kernels are the Markov kernels. Let ( S , S ) {\displaystyle (S,{\mathcal {S}})} , ( T , T...

Hierarchical hidden Markov model Maximum-entropy Markov model Variable-order Markov model Markov renewal process Markov chain mixing time Markov kernel Piecewise-deterministic...

the Markov operator admits a kernel representation. Markov operators can be linear or non-linear. Closely related to Markov operators is the Markov semigroup...

distribution is a parametrized family of probability measures called a Markov kernel. Consider two random variables X , Y : Ω → R {\displaystyle X,Y:\Omega...

process Markov information source Markov kernel Markov logic network Markov model Markov network Markov process Markov property Markov random field Markov renewal...

contains examples of Markov chains and Markov processes in action. All examples are in the countable state space. For an overview of Markov chains in general...

stochastic matrix is a square matrix used to describe the transitions of a Markov chain. Each of its entries is a nonnegative real number representing a probability...

In machine learning, kernel machines are a class of algorithms for pattern analysis, whose best known member is the support-vector machine (SVM). These...

In statistical classification, the Fisher kernel, named after Ronald Fisher, is a function that measures the similarity of two objects on the basis of...

a protein fragment Heterogeneous memory management, in the Linux kernel Hidden Markov model, a statistical model Central Mashan Miao language (ISO 639-3...

LogitBoost Manifold alignment Markov chain Monte Carlo (MCMC) Minimum redundancy feature selection Mixture of experts Multiple kernel learning Non-negative matrix...

In machine learning, the kernel embedding of distributions (also called the kernel mean or mean map) comprises a class of nonparametric methods in which...

(1_{X\in B}|{\mathcal {H}})(\omega )} . It can be shown that they form a Markov kernel, that is, for almost all ω {\displaystyle \omega } , κ H ( ω , − ) {\displaystyle...

processes there is the related concept of a stochastic kernel, probability kernel, Markov kernel. Define M ~ := { μ ∣ μ  is measure on  ( E , E ) } {\displaystyle...

adaptive kernel estimates. Davies and Ghahramani proposed Random Forest Kernel and show that it can empirically outperform state-of-art kernel methods...

{\displaystyle 1_{T^{-1}(S)}} . Similarly, given a measure-preserving Markov kernel k : ( X , F , p ) → ( X , F , p ) {\displaystyle k:(X,{\mathcal {F}}...

efficiently perform a non-linear classification using what is called the kernel trick, which represent the data only through a set of pairwise similarity...

^{i-1},{\mathcal {A}}^{i-1})\to (\Omega _{i},{\mathcal {A}}_{i})} be the Markov kernel derived from ( Ω i − 1 , A i − 1 ) {\displaystyle (\Omega ^{i-1},{\mathcal...

probability kernels { Λ n } n = 1 N {\displaystyle \{\Lambda ^{n}\}_{n=1}^{N}} such that θ k 1 {\displaystyle \theta _{k}^{1}} is a Markov chain with transition...

kernel machines such as support vector machines for regression and classification problems. Shogun also offers a full implementation of Hidden Markov...

balance in kinetics seem to be clear. A Markov process is called a reversible Markov process or reversible Markov chain if it satisfies the detailed balance...

trees Gaussian Mixture Models (GMMs) Hidden Markov Models (HMMs) Kernel density estimation (KDE) Kernel Principal Component Analysis (KPCA) K-Means Clustering...

that the kernel captures some local geometry of data set. The Markov chain defines fast and slow directions of propagation through the kernel values. As...

In machine learning, the kernel perceptron is a variant of the popular perceptron learning algorithm that can learn kernel machines, i.e. non-linear classifiers...

Artificial neural networks Game theory Bayesian probability Consciousness Markov kernels Signal-flow graphs Conjunctive queries Bidirectional transformations...

The Lempel–Ziv–Markov chain algorithm (LZMA) is an algorithm used to perform lossless data compression. It has been under development since either 1996...

} . A superprocess has a number of properties. It is a Markov process, and its Markov kernel Q t ( μ , d ν ) {\displaystyle Q_{t}(\mu ,d\nu )} verifies...