Eine Markow-Kette (englisch Markov chain; auch Markow-Prozess, nach Andrei Andrejewitsch Markow; andere Schreibweisen Markov-Kette, Markoff-Kette. Eine Markow-Kette ist ein spezieller stochastischer Prozess. Ziel bei der Anwendung von Markow-Ketten ist es, Wahrscheinlichkeiten für das Eintreten zukünftiger Ereignisse anzugeben. Handelt es sich um einen zeitdiskreten Prozess, wenn also X(t) nur abzählbar viele Werte annehmen kann, so heißt Dein Prozess Markov-Kette. Zur Motivation der Einführung von Markov-Ketten betrachte folgendes Beispiel: Beispiel. Wir wollen die folgende Situation mathematisch formalisieren: Eine. In diesem Vortrag werden die Mittelwertsregeln eingeführt, mit deren Hilfe viele Probleme, die als absorbierende Markov-Kette gesehen werden, einfach gelöst.
Markow-Ketten. Leitfragen. Wie können wir Texte handhabbar modellieren? Was ist die Markov-Bedingung und warum macht sie unser Leben erheblich leichter? Definition: Diskrete Markovkette. Ein stochastischer Prozeß (Xn)n∈IN mit diskretem Zustandsraum S heißt zeit- diskrete Markovkette (Discrete–Time Markov. In diesem Vortrag werden die Mittelwertsregeln eingeführt, mit deren Hilfe viele Probleme, die als absorbierende Markov-Kette gesehen werden, einfach gelöst. Markow-Ketten. Leitfragen. Wie können wir Texte handhabbar modellieren? Was ist die Markov-Bedingung und warum macht sie unser Leben erheblich leichter? Eine Markow-Kette ist ein stochastischer Prozess, mit dem sich die Wahrscheinlichkeiten für das Eintreten bestimmter Zustände bestimmen lässt. In Form eines. Markov-Ketten sind stochastische Prozesse, die sich durch ihre „Gedächtnislosigkeit“ auszeichnen. Konkret bedeutet dies, dass für die Entwicklung des. Definition: Diskrete Markovkette. Ein stochastischer Prozeß (Xn)n∈IN mit diskretem Zustandsraum S heißt zeit- diskrete Markovkette (Discrete–Time Markov. Dabei ist eine Markow-Kette durch die Startverteilung auf dem Zustandsraum und den stochastischen Kern auch Übergangskern oder Markowkern schon eindeutig bestimmt. Ist es aber bewölkt, so regnet es mit Wahrscheinlichkeit 0,5 am folgenden Tag und mit Wahrscheinlichkeit von 0,5 scheint die Sonne. Wenn du diesen Cookie deaktivierst, können wir die Einstellungen nicht speichern. Klassen Festival Hoppegarten kann Zustände in Klassen zusammenfassen und Beste Spielothek in Pickel finden die Klassen separat, losgelöst von der gesamten Markov-Kette betrachten. Markov chain Markov chain in the process diagram. Diese besagt, in welcher Wahrscheinlichkeit die Markov-Kette in welchem Zustand startet.
Markov-Kette VideoAbsorptionswahrscheinlichkeiten, Markow-Kette, Markov-Kette, Markoff-Kette - Mathe by Daniel Jung
Using this analysis, you can generate a new sequence of random but related events, which will look similar to the original.
A Markov process is useful for analyzing dependent random events - that is, events whose likelihood depends on what happened last.
It would NOT be a good way to model a coin flip, for example, since every time you toss the coin, it has no memory of what happened before.
The sequence of heads and tails are not inter-related. They are independent events. But many random events are affected by what happened before.
For example, yesterday's weather does have an influence on what today's weather is. They are not independent events. A Markov model could look at a long sequence of rainy and sunny days, and analyze the likelihood that one kind of weather gets followed by another kind.
Given this analysis, we could generate a new sequence of statistically similar weather by following these steps:. In other words, the "output chain" would reflect statistically the transition probabilities derived from weather we observed.
This stream of events is called a Markov Chain. A Markov Chain, while similar to the source in the small, is often nonsensical in the large.
Random walks based on integers and the gambler's ruin problem are examples of Markov processes. From any position there are two possible transitions, to the next or previous integer.
The transition probabilities depend only on the current position, not on the manner in which the position was reached.
For example, the transition probabilities from 5 to 4 and 5 to 6 are both 0. These probabilities are independent of whether the system was previously in 4 or 6.
Another example is the dietary habits of a creature who eats only grapes, cheese, or lettuce, and whose dietary habits conform to the following rules:.
This creature's eating habits can be modeled with a Markov chain since its choice tomorrow depends solely on what it ate today, not what it ate yesterday or any other time in the past.
One statistical property that could be calculated is the expected percentage, over a long period, of the days on which the creature will eat grapes.
A series of independent events for example, a series of coin flips satisfies the formal definition of a Markov chain. However, the theory is usually applied only when the probability distribution of the next step depends non-trivially on the current state.
To see why this is the case, suppose that in the first six draws, all five nickels and a quarter are drawn. However, it is possible to model this scenario as a Markov process.
This new model would be represented by possible states that is, 6x6x6 states, since each of the three coin types could have zero to five coins on the table by the end of the 6 draws.
After the second draw, the third draw depends on which coins have so far been drawn, but no longer only on the coins that were drawn for the first state since probabilistically important information has since been added to the scenario.
A discrete-time Markov chain is a sequence of random variables X 1 , X 2 , X 3 , The possible values of X i form a countable set S called the state space of the chain.
The elements q ii are chosen such that each row of the transition rate matrix sums to zero, while the row-sums of a probability transition matrix in a discrete Markov chain are all equal to one.
There are three equivalent definitions of the process. Define a discrete-time Markov chain Y n to describe the n th jump of the process and variables S 1 , S 2 , S 3 , If the state space is finite , the transition probability distribution can be represented by a matrix , called the transition matrix, with the i , j th element of P equal to.
Since each row of P sums to one and all elements are non-negative, P is a right stochastic matrix. By comparing this definition with that of an eigenvector we see that the two concepts are related and that.
If there is more than one unit eigenvector then a weighted sum of the corresponding stationary states is also a stationary state. But for a Markov chain one is usually more interested in a stationary state that is the limit of the sequence of distributions for some initial distribution.
If the Markov chain is time-homogeneous, then the transition matrix P is the same after each step, so the k -step transition probability can be computed as the k -th power of the transition matrix, P k.
This is stated by the Perron—Frobenius theorem. Because there are a number of different special cases to consider, the process of finding this limit if it exists can be a lengthy task.
However, there are many techniques that can assist in finding this limit. Multiplying together stochastic matrices always yields another stochastic matrix, so Q must be a stochastic matrix see the definition above.
It is sometimes sufficient to use the matrix equation above and the fact that Q is a stochastic matrix to solve for Q.
Here is one method for doing so: first, define the function f A to return the matrix A with its right-most column replaced with all 1's.
One thing to notice is that if P has an element P i , i on its main diagonal that is equal to 1 and the i th row or column is otherwise filled with 0's, then that row or column will remain unchanged in all of the subsequent powers P k.
Hence, the i th row or column of Q will have the 1 and the 0's in the same positions as in P. Then assuming that P is diagonalizable or equivalently that P has n linearly independent eigenvectors, speed of convergence is elaborated as follows.
For non-diagonalizable, that is, defective matrices , one may start with the Jordan normal form of P and proceed with a bit more involved set of arguments in a similar way.
Then by eigendecomposition. Since P is a row stochastic matrix, its largest left eigenvalue is 1. That means. Many results for Markov chains with finite state space can be generalized to chains with uncountable state space through Harris chains.
The main idea is to see if there is a point in the state space that the chain hits with probability one. Lastly, the collection of Harris chains is a comfortable level of generality, which is broad enough to contain a large number of interesting examples, yet restrictive enough to allow for a rich theory.
The use of Markov chains in Markov chain Monte Carlo methods covers cases where the process follows a continuous state space.
Considering a collection of Markov chains whose evolution takes in account the state of other Markov chains, is related to the notion of locally interacting Markov chains.
This corresponds to the situation when the state space has a Cartesian- product form. See interacting particle system and stochastic cellular automata probabilistic cellular automata.
See for instance Interaction of Markov Processes  or . Two states communicate with each other if both are reachable from one another by a sequence of transitions that have positive probability.
This is an equivalence relation which yields a set of communicating classes. A class is closed if the probability of leaving the class is zero.
A Markov chain is irreducible if there is one communicating class, the state space. That is:. A state i is said to be transient if, starting from i , there is a non-zero probability that the chain will never return to i.
It is recurrent otherwise. For a recurrent state i , the mean hitting time is defined as:. Periodicity, transience, recurrence and positive and null recurrence are class properties—that is, if one state has the property then all states in its communicating class have the property.
A state i is said to be ergodic if it is aperiodic and positive recurrent. In other words, a state i is ergodic if it is recurrent, has a period of 1 , and has finite mean recurrence time.
If all states in an irreducible Markov chain are ergodic, then the chain is said to be ergodic. It can be shown that a finite state irreducible Markov chain is ergodic if it has an aperiodic state.
More generally, a Markov chain is ergodic if there is a number N such that any state can be reached from any other state in any number of steps less or equal to a number N.
A Markov chain with more than one state and just one out-going transition per state is either not irreducible or not aperiodic, hence cannot be ergodic.
In some cases, apparently non-Markovian processes may still have Markovian representations, constructed by expanding the concept of the 'current' and 'future' states.
For example, let X be a non-Markovian process. Then define a process Y , such that each state of Y represents a time-interval of states of X.
Mathematically, this takes the form:. An example of a non-Markovian process with a Markovian representation is an autoregressive time series of order greater than one.
The hitting time is the time, starting in a given set of states until the chain arrives in a given state or set of states. The distribution of such a time period has a phase type distribution.
The simplest such distribution is that of a single exponentially distributed transition. By Kelly's lemma this process has the same stationary distribution as the forward process.
A chain is said to be reversible if the reversed process is the same as the forward process. Kolmogorov's criterion states that the necessary and sufficient condition for a process to be reversible is that the product of transition rates around a closed loop must be the same in both directions.
Strictly speaking, the EMC is a regular discrete-time Markov chain, sometimes referred to as a jump process.
Each element of the one-step transition probability matrix of the EMC, S , is denoted by s ij , and represents the conditional probability of transitioning from state i into state j.
These conditional probabilities may be found by. S may be periodic, even if Q is not. Markov models are used to model changing systems.
There are 4 main types of models, that generalize Markov chains depending on whether every sequential state is observable or not, and whether the system is to be adjusted on the basis of observations made:.
A Bernoulli scheme is a special case of a Markov chain where the transition probability matrix has identical rows, which means that the next state is even independent of the current state in addition to being independent of the past states.
A Bernoulli scheme with only two possible states is known as a Bernoulli process. Research has reported the application and usefulness of Markov chains in a wide range of topics such as physics, chemistry, biology, medicine, music, game theory and sports.
Markovian systems appear extensively in thermodynamics and statistical mechanics , whenever probabilities are used to represent unknown or unmodelled details of the system, if it can be assumed that the dynamics are time-invariant, and that no relevant history need be considered which is not already included in the state description.
Therefore, Markov Chain Monte Carlo method can be used to draw samples randomly from a black-box to approximate the probability distribution of attributes over a range of objects.
The paths, in the path integral formulation of quantum mechanics, are Markov chains. Markov chains are used in lattice QCD simulations. A reaction network is a chemical system involving multiple reactions and chemical species.
The simplest stochastic models of such networks treat the system as a continuous time Markov chain with the state being the number of molecules of each species and with reactions modeled as possible transitions of the chain.
For example, imagine a large number n of molecules in solution in state A, each of which can undergo a chemical reaction to state B with a certain average rate.
Perhaps the molecule is an enzyme, and the states refer to how it is folded. The state of any single enzyme follows a Markov chain, and since the molecules are essentially independent of each other, the number of molecules in state A or B at a time is n times the probability a given molecule is in that state.
The classical model of enzyme activity, Michaelis—Menten kinetics , can be viewed as a Markov chain, where at each time step the reaction proceeds in some direction.
While Michaelis-Menten is fairly straightforward, far more complicated reaction networks can also be modeled with Markov chains. An algorithm based on a Markov chain was also used to focus the fragment-based growth of chemicals in silico towards a desired class of compounds such as drugs or natural products.
It is not aware of its past that is, it is not aware of what is already bonded to it. It then transitions to the next state when a fragment is attached to it.
The transition probabilities are trained on databases of authentic classes of compounds. Also, the growth and composition of copolymers may be modeled using Markov chains.
Based on the reactivity ratios of the monomers that make up the growing polymer chain, the chain's composition may be calculated for example, whether monomers tend to add in alternating fashion or in long runs of the same monomer.
Due to steric effects , second-order Markov effects may also play a role in the growth of some polymer chains.
Similarly, it has been suggested that the crystallization and growth of some epitaxial superlattice oxide materials can be accurately described by Markov chains.
Several theorists have proposed the idea of the Markov chain statistical test MCST , a method of conjoining Markov chains to form a " Markov blanket ", arranging these chains in several recursive layers "wafering" and producing more efficient test sets—samples—as a replacement for exhaustive testing.
MCSTs also have uses in temporal state-based networks; Chilukuri et al. Solar irradiance variability assessments are useful for solar power applications.
Solar irradiance variability at any location over time is mainly a consequence of the deterministic variability of the sun's path across the sky dome and the variability in cloudiness.
The variability of accessible solar irradiance on Earth's surface has been modeled using Markov chains,     also including modeling the two states of clear and cloudiness as a two-state Markov chain.
Hidden Markov models are the basis for most modern automatic speech recognition systems. Markov chains are used throughout information processing.
Claude Shannon 's famous paper A Mathematical Theory of Communication , which in a single step created the field of information theory , opens by introducing the concept of entropy through Markov modeling of the English language.
Such idealized models can capture many of the statistical regularities of systems. Even without describing the full structure of the system perfectly, such signal models can make possible very effective data compression through entropy encoding techniques such as arithmetic coding.
They also allow effective state estimation and pattern recognition. Markov chains also play an important role in reinforcement learning. Markov chains are also the basis for hidden Markov models, which are an important tool in such diverse fields as telephone networks which use the Viterbi algorithm for error correction , speech recognition and bioinformatics such as in rearrangements detection .
The LZMA lossless data compression algorithm combines Markov chains with Lempel-Ziv compression to achieve very high compression ratios.
Markov chains are the basis for the analytical treatment of queues queueing theory. Agner Krarup Erlang initiated the subject in Numerous queueing models use continuous-time Markov chains.
The PageRank of a webpage as used by Google is defined by a Markov chain. Markov models have also been used to analyze web navigation behavior of users.
A user's web link transition on a particular website can be modeled using first- or second-order Markov models and can be used to make predictions regarding future navigation and to personalize the web page for an individual user.
Markov chain methods have also become very important for generating sequences of random numbers to accurately reflect very complicated desired probability distributions, via a process called Markov chain Monte Carlo MCMC.
In recent years this has revolutionized the practicability of Bayesian inference methods, allowing a wide range of posterior distributions to be simulated and their parameters found numerically.
Markov chains are used in finance and economics to model a variety of different phenomena, including asset prices and market crashes. The first financial model to use a Markov chain was from Prasad et al.
Hamilton , in which a Markov chain is used to model switches between periods high and low GDP growth or alternatively, economic expansions and recessions.
Calvet and Adlai J. Fisher, which builds upon the convenience of earlier regime-switching models. Dynamic macroeconomics heavily uses Markov chains.
An example is using Markov chains to exogenously model prices of equity stock in a general equilibrium setting.
Credit rating agencies produce annual tables of the transition probabilities for bonds of different credit ratings. Markov chains are generally used in describing path-dependent arguments, where current structural configurations condition future outcomes.
An example is the reformulation of the idea, originally due to Karl Marx 's Das Kapital , tying economic development to the rise of capitalism.
In current research, it is common to use a Markov chain to model how once a country reaches a specific level of economic development, the configuration of structural factors, such as size of the middle class , the ratio of urban to rural residence, the rate of political mobilization, etc.
Markov chains can be used to model many games of chance. Cherry-O ", for example, are represented exactly by Markov chains. At each turn, the player starts in a given state on a given square and from there has fixed odds of moving to certain other states squares.
Markov chains are employed in algorithmic music composition , particularly in software such as Csound , Max , and SuperCollider. In a first-order chain, the states of the system become note or pitch values, and a probability vector for each note is constructed, completing a transition probability matrix see below.
An algorithm is constructed to produce output note values based on the transition matrix weightings, which could be MIDI note values, frequency Hz , or any other desirable metric.
A second-order Markov chain can be introduced by considering the current state and also the previous state, as indicated in the second table. Higher, n th-order chains tend to "group" particular notes together, while 'breaking off' into other patterns and sequences occasionally.
These higher-order chains tend to generate results with a sense of phrasal structure, rather than the 'aimless wandering' produced by a first-order system.
Markov chains can be used structurally, as in Xenakis's Analogique A and B. Usually musical systems need to enforce specific control constraints on the finite-length sequences they generate, but control constraints are not compatible with Markov models, since they induce long-range dependencies that violate the Markov hypothesis of limited memory.
In order to overcome this limitation, a new approach has been proposed. Markov chain models have been used in advanced baseball analysis since , although their use is still rare.
Each half-inning of a baseball game fits the Markov chain state when the number of runners and outs are considered.
During any at-bat, there are 24 possible combinations of number of outs and position of the runners. Mark Pankin shows that Markov chain models can be used to evaluate runs created for both individual players as well as a team.
Markov processes can also be used to generate superficially real-looking text given a sample document. Markov processes are used in a variety of recreational " parody generator " software see dissociated press , Jeff Harrison,  Mark V.
Shaney ,   and Academias Neutronium. Markov chains have been used for forecasting in several areas: for example, price trends,  wind power,  and solar irradiance.
From Wikipedia, the free encyclopedia. Mathematical system. Main article: Examples of Markov chains. See also: Kolmogorov equations Markov jump process.
This section includes a list of references , related reading or external links , but its sources remain unclear because it lacks inline citations.
Please help to improve this section by introducing more precise citations. February Learn how and when to remove this template message. Main article: Markov chains on a measurable state space.
Main article: Phase-type distribution. Main article: Markov model. Main article: Bernoulli scheme. Michaelis-Menten kinetics. The enzyme E binds a substrate S and produces a product P.
Each reaction is a state transition in a Markov chain. Main article: Queueing theory. Dynamics of Markovian particles Markov chain approximation method Markov chain geostatistics Markov chain mixing time Markov decision process Markov information source Markov random field Quantum Markov chain Semi-Markov process Stochastic cellular automaton Telescoping Markov chain Variable-order Markov model.
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