SettingsMarkov models
Absorbing Markov chain
An absorbing Markov chain is a Markov chain in which every state can reach an absorbing state.
An absorbing Markov chain is a Markov chain in which every state can reach an absorbing state.
Algorithmic composition
Algorithmic composition is the technique of using algorithms to create music.
Algorithmic composition is the technique of using algorithms to create music.
Baum-Welch algorithm
In electrical engineering, computer science, statistical computing and bioinformatics, the Baum–Welch algorithm is used to find the unknown parameters of a hidden Markov model (HMM).
In electrical engineering, computer science, statistical computing and bioinformatics, the Baum–Welch algorithm is used to find the unknown parameters of a hidden Markov model (HMM).
Baum–Welch algorithm
In electrical engineering, computer science, statistical computing and bioinformatics, the Baum–Welch algorithm is used to find the unknown parameters of a hidden Markov model (HMM).
In electrical engineering, computer science, statistical computing and bioinformatics, the Baum–Welch algorithm is used to find the unknown parameters of a hidden Markov model (HMM).
Bayesian inference in phylogeny
Bayesian inference in phylogeny generates a posterior distribution for a parameter, composed of a phylogenetic tree and a model of evolution, based on the prior for that parameter and the likeli...
Bayesian inference in phylogeny generates a posterior distribution for a parameter, composed of a phylogenetic tree and a model of evolution, based on the prior for that parameter and the likeli...
Bernoulli scheme
In mathematics, the Bernoulli scheme or Bernoulli shift is a generalization of the Bernoulli process to more than two possible outcomes.
In mathematics, the Bernoulli scheme or Bernoulli shift is a generalization of the Bernoulli process to more than two possible outcomes.
Burst error
In telecommunication, a burst error or error burst is a contiguous sequence of symbols, received over a data transmission channel, such that the first and last symbols are in error and the...
In telecommunication, a burst error or error burst is a contiguous sequence of symbols, received over a data transmission channel, such that the first and last symbols are in error and the...
Dependability state model
A dependability state diagram is a method for modelling a system as a Markov chain.
A dependability state diagram is a method for modelling a system as a Markov chain.
Detailed balance
The principle of detailed balance is formulated for kinetic systems which are decomposed into elementary processes (collisions, or steps, or elementary reactions): At equilibrium, each element...
The principle of detailed balance is formulated for kinetic systems which are decomposed into elementary processes (collisions, or steps, or elementary reactions): At equilibrium, each element...
Discrete phase-type distribution
The discrete phase-type distribution is a probability distribution that results from a system of one or more inter-related geometric distributions occurring in sequence, or phases.
The discrete phase-type distribution is a probability distribution that results from a system of one or more inter-related geometric distributions occurring in sequence, or phases.
Dynamic Markov compression
Dynamic Markov compression (DMC) is a lossless data compression algorithm developed by Gordon Cormack and Nigel Horspool.
Dynamic Markov compression (DMC) is a lossless data compression algorithm developed by Gordon Cormack and Nigel Horspool.
Dynamics of Markovian particles
Dynamics of Markovian particles (DMP) is the basis of a theory for kinetics of particles in open heterogeneous systems.
Dynamics of Markovian particles (DMP) is the basis of a theory for kinetics of particles in open heterogeneous systems.
Entropy rate
In the mathematical theory of probability, the entropy rate or source information rate of a stochastic process is, informally, the time density of the average information in a stochastic p...
In the mathematical theory of probability, the entropy rate or source information rate of a stochastic process is, informally, the time density of the average information in a stochastic p...
Forward algorithm
The forward algorithm, in the context of a hidden Markov model, is used to calculate a 'belief state': the probability of a state at a certain time, given the history of evidence.
The forward algorithm, in the context of a hidden Markov model, is used to calculate a 'belief state': the probability of a state at a certain time, given the history of evidence.
Gene prediction
In computational biology gene prediction or gene finding refers to the proces of identifying the regions of genomic DNA that encode genes.
In computational biology gene prediction or gene finding refers to the proces of identifying the regions of genomic DNA that encode genes.
GLIMMER
GLIMMER (Gene Locator and Interpolated Markov ModelER) was the first bioinformatics system for finding genes that used the interpolated Markov model formalism.
GLIMMER (Gene Locator and Interpolated Markov ModelER) was the first bioinformatics system for finding genes that used the interpolated Markov model formalism.
Google matrix
A Google matrix is a particular stochastic matrix that is used by Google's PageRank algorithm.
A Google matrix is a particular stochastic matrix that is used by Google's PageRank algorithm.
Hidden Markov model
A hidden Markov model is a statistical Markov model in which the system being modeled is assumed to be a Markov process with unobserved states.
A hidden Markov model is a statistical Markov model in which the system being modeled is assumed to be a Markov process with unobserved states.
Hidden semi-Markov model
A hidden semi-Markov model (HSMM) is a statistical model with the same structure as a hidden Markov model except that the unobservable process is semi-Markov rather than Markov.
A hidden semi-Markov model (HSMM) is a statistical model with the same structure as a hidden Markov model except that the unobservable process is semi-Markov rather than Markov.
Hierarchical hidden Markov model
The hierarchical hidden Markov model (HHMM) is a statistical model derived from the hidden Markov model (HMM).
The hierarchical hidden Markov model (HHMM) is a statistical model derived from the hidden Markov model (HMM).
Iterative Viterbi decoding
Iterative Viterbi decoding is an algorithm that spots the subsequence S of an observation O = {o1, ..., on} having the highest average probability (i.e., probability ...
Iterative Viterbi decoding is an algorithm that spots the subsequence S of an observation O = {o1, ..., on} having the highest average probability (i.e., probability ...
Kalman filter
The Kalman filter, also known as linear quadratic estimation (LQE), is an algorithm which uses a series of measurements observed over time, containing noise (random variations) and other i...
The Kalman filter, also known as linear quadratic estimation (LQE), is an algorithm which uses a series of measurements observed over time, containing noise (random variations) and other i...
Kolmogorov backward equations (diffusion)
The Kolmogorov backward equation (KBE) (diffusion) and its adjoint sometimes known as the Kolmogorov forward equation (diffusion) are partial differential equations (PDE) that arise in the theor...
The Kolmogorov backward equation (KBE) (diffusion) and its adjoint sometimes known as the Kolmogorov forward equation (diffusion) are partial differential equations (PDE) that arise in the theor...
Layered hidden Markov model
The layered hidden Markov model (LHMM) is a statistical model derived from the hidden Markov model (HMM).
The layered hidden Markov model (LHMM) is a statistical model derived from the hidden Markov model (HMM).
Mark V Shaney
Mark V Shaney is a fake Usenet user whose postings were generated by using Markov chain techniques.
Mark V Shaney is a fake Usenet user whose postings were generated by using Markov chain techniques.
Markov chain
A Markov chain, named after Andrey Markov, is a mathematical system that undergoes transitions from one state to another, between a finite or countable number of possible states.
A Markov chain, named after Andrey Markov, is a mathematical system that undergoes transitions from one state to another, between a finite or countable number of possible states.
Markov chain geostatistics
Markov chain geostatistics refer to the Markov chain models, simulation algorithms and associated spatial correlation measures (e.g., transiogram) based on the Markov chain random field theory, ...
Markov chain geostatistics refer to the Markov chain models, simulation algorithms and associated spatial correlation measures (e.g., transiogram) based on the Markov chain random field theory, ...
Markov chain Monte Carlo
Markov chain Monte Carlo methods are a class of algorithms for sampling from probability distributions based on constructing a Markov chain that has the desired distribution as its equilibrium d...
Markov chain Monte Carlo methods are a class of algorithms for sampling from probability distributions based on constructing a Markov chain that has the desired distribution as its equilibrium d...
Markov model
In probability theory, a Markov model is a stochastic model that assumes the Markov property.
In probability theory, a Markov model is a stochastic model that assumes the Markov property.
Markov partition
A Markov partition is a tool used in dynamical systems theory, allowing the methods of symbolic dynamics to be applied to the study of hyperbolic systems.
A Markov partition is a tool used in dynamical systems theory, allowing the methods of symbolic dynamics to be applied to the study of hyperbolic systems.
Markov property
In probability theory and statistics, the term Markov property refers to the memoryless property of a stochastic process.
In probability theory and statistics, the term Markov property refers to the memoryless property of a stochastic process.
Markov switching multifractal
In financial econometrics, the Markov-switching multifractal (MSM) is a model of asset returns that incorporates stochastic volatility components of heterogeneous durations.
In financial econometrics, the Markov-switching multifractal (MSM) is a model of asset returns that incorporates stochastic volatility components of heterogeneous durations.
Markovian discrimination
Markovian discrimination in spam filtering is a method used in CRM114 and other spam filters to model the statistical behaviors of spam and nonspam more accurately than in simple Bayesian methods.
Markovian discrimination in spam filtering is a method used in CRM114 and other spam filters to model the statistical behaviors of spam and nonspam more accurately than in simple Bayesian methods.
Maximum entropy Markov model
In machine learning, a maximum entropy Markov model (MEMM), or conditional Markov model (CMM), is a graphical model for sequence labeling that combines features of hidden Marko...
In machine learning, a maximum entropy Markov model (MEMM), or conditional Markov model (CMM), is a graphical model for sequence labeling that combines features of hidden Marko...
Maximum-entropy Markov model
In machine learning, a maximum-entropy Markov model, or conditional Markov model, is a graphical model for sequence labeling that combines features of hidden Markov models and maximum entr...
In machine learning, a maximum-entropy Markov model, or conditional Markov model, is a graphical model for sequence labeling that combines features of hidden Markov models and maximum entr...
Models of DNA evolution
A number of different Markov models of DNA sequence evolution have been proposed.
A number of different Markov models of DNA sequence evolution have been proposed.
Multiple sequence alignment
A multiple sequence alignment (MSA) is a sequence alignment of three or more biological sequences, generally protein, DNA, or RNA. In many cases, the input set of query sequences are assumed to ...
A multiple sequence alignment (MSA) is a sequence alignment of three or more biological sequences, generally protein, DNA, or RNA. In many cases, the input set of query sequences are assumed to ...
PageRank
PageRank is a link analysis algorithm, named after Larry Page and used by the Google Internet search engine, that assigns a numerical weighting to each element of a hyperlinked set of documents,...
PageRank is a link analysis algorithm, named after Larry Page and used by the Google Internet search engine, that assigns a numerical weighting to each element of a hyperlinked set of documents,...
Part-of-speech tagging
In corpus linguistics, part-of-speech tagging, also called grammatical tagging or word-category disambiguation, is the process of marking up a word in a text as corresponding to a pa...
In corpus linguistics, part-of-speech tagging, also called grammatical tagging or word-category disambiguation, is the process of marking up a word in a text as corresponding to a pa...
Path dependence
Path dependence explains how the set of decisions one faces for any given circumstance is limited by the decisions one has made in the past, even though past circumstances may no longer be relevant.
Path dependence explains how the set of decisions one faces for any given circumstance is limited by the decisions one has made in the past, even though past circumstances may no longer be relevant.
Poisson hidden Markov model
In statistics, Poisson hidden Markov models (PHMM) are a special case of hidden Markov models where a Poisson process has a rate which varies in association with changes between the different st...
In statistics, Poisson hidden Markov models (PHMM) are a special case of hidden Markov models where a Poisson process has a rate which varies in association with changes between the different st...
Poisson process
A Poisson process, named after the French mathematician Siméon-Denis Poisson (1781–1840), is a stochastic process in which events occur continuously and independently of one another (the w...
A Poisson process, named after the French mathematician Siméon-Denis Poisson (1781–1840), is a stochastic process in which events occur continuously and independently of one another (the w...
Pop music automation
Pop Music Automation is a field of study among musicians and computer scientists with a goal of producing successful pop music algorithmically.
Pop Music Automation is a field of study among musicians and computer scientists with a goal of producing successful pop music algorithmically.
Population process
In applied probability, a population process is a Markov chain in which the state of the chain is analogous to the number of individuals in a population (0, 1, 2, etc.), and changes to the state...
In applied probability, a population process is a Markov chain in which the state of the chain is analogous to the number of individuals in a population (0, 1, 2, etc.), and changes to the state...
Quantum Markov chain
In mathematics, the quantum Markov chain is a reformulation of the ideas of a classical Markov chain, replacing the classical definitions of probability with quantum probability.
In mathematics, the quantum Markov chain is a reformulation of the ideas of a classical Markov chain, replacing the classical definitions of probability with quantum probability.
Queueing model
In queueing theory, a queueing model is used to approximate a real queueing situation or system, so the queueing behaviour can be analysed mathematically.
In queueing theory, a queueing model is used to approximate a real queueing situation or system, so the queueing behaviour can be analysed mathematically.
Queueing theory
Queueing theory is the mathematical study of waiting lines, or queues.
Queueing theory is the mathematical study of waiting lines, or queues.
Reinforcement learning
Inspired by behaviorist psychology, reinforcement learning is an area of machine learning in computer science, concerned with how an agent ought to take actions in an environment so...
Inspired by behaviorist psychology, reinforcement learning is an area of machine learning in computer science, concerned with how an agent ought to take actions in an environment so...
Snakes and ladders
Snakes and Ladders is an ancient Indian board game regarded today as a worldwide classic.
Snakes and Ladders is an ancient Indian board game regarded today as a worldwide classic.
Snakes and Ladders
Snakes and Ladders is an ancient Indian board game regarded today as a worldwide classic.
Snakes and Ladders is an ancient Indian board game regarded today as a worldwide classic.
Soft output Viterbi algorithm
The soft output Viterbi algorithm (SOVA) is a variant of the classical Viterbi algorithm.
The soft output Viterbi algorithm (SOVA) is a variant of the classical Viterbi algorithm.
Stochastic matrix
In mathematics, a stochastic matrix (also termed probability matrix, transition matrix, substitution matrix, or Markov matrix) is a matrix used to describe the transition...
In mathematics, a stochastic matrix (also termed probability matrix, transition matrix, substitution matrix, or Markov matrix) is a matrix used to describe the transition...
Subshift of finite type
In mathematics, subshifts of finite type are used to model dynamical systems, and in particular are the objects of study in symbolic dynamics and ergodic theory.
In mathematics, subshifts of finite type are used to model dynamical systems, and in particular are the objects of study in symbolic dynamics and ergodic theory.
Transiogram
Transiogram is the accompanying spatial correlation measure of Markov chain random fields and an important part of Markov chain geostatistics.
Transiogram is the accompanying spatial correlation measure of Markov chain random fields and an important part of Markov chain geostatistics.
Variable-order Bayesian network
Variable-order Bayesian network (VOBN) models provide an important extension of both the Bayesian network models and the variable-order Markov models.
Variable-order Bayesian network (VOBN) models provide an important extension of both the Bayesian network models and the variable-order Markov models.
Variable-order Markov model
Variable-order Markov (VOM) models are an important class of models that extend the well known Markov chain models.
Variable-order Markov (VOM) models are an important class of models that extend the well known Markov chain models.
Viterbi algorithm
The Viterbi algorithm is a dynamic programming algorithm for finding the most likely sequence of hidden states – called the Viterbi path – that results in a sequence of observe...
The Viterbi algorithm is a dynamic programming algorithm for finding the most likely sequence of hidden states – called the Viterbi path – that results in a sequence of observe...