Markov Chains Theory And Applications Pdf

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A Markov chain is a stochastic model describing a sequence of possible events in which the probability of each event depends only on the state attained in the previous event. It is named after the Russian mathematician Andrey Markov. Markov chains have many applications as statistical models of real-world processes, [1] [4] [5] [6] such as studying cruise control systems in motor vehicles , queues or lines of customers arriving at an airport, currency exchange rates and animal population dynamics.

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This paper proposes an extension of a single coupled Markov chain model to characterize heterogeneity of geological formations, and to make conditioning on any number of well data possible. The methodology is based on the concept of conditioning a Markov chain on the future states. Because the conditioning is performed in an explicit way, the methodology is efficient in terms of computer time and storage. Applications to synthetic and field data show good results.

A Markov Chain Model for Changes in Users’ Assessment of Search Results

A Markov chain is a stochastic model describing a sequence of possible events in which the probability of each event depends only on the state attained in the previous event. It is named after the Russian mathematician Andrey Markov. Markov chains have many applications as statistical models of real-world processes, [1] [4] [5] [6] such as studying cruise control systems in motor vehicles , queues or lines of customers arriving at an airport, currency exchange rates and animal population dynamics.

Markov processes are the basis for general stochastic simulation methods known as Markov chain Monte Carlo , which are used for simulating sampling from complex probability distributions, and have found application in Bayesian statistics , thermodynamics , statistical mechanics , physics , chemistry , economics , finance , signal processing , information theory and artificial intelligence.

The adjective Markovian is used to describe something that is related to a Markov process. A Markov process is a stochastic process that satisfies the Markov property [1] sometimes characterized as " memorylessness ". In simpler terms, it is a process for which predictions can be made regarding future outcomes based solely on its present state and—most importantly—such predictions are just as good as the ones that could be made knowing the process's full history.

A Markov chain is a type of Markov process that has either a discrete state space or a discrete index set often representing time , but the precise definition of a Markov chain varies. The system's state space and time parameter index need to be specified. The following table gives an overview of the different instances of Markov processes for different levels of state space generality and for discrete time v.

Note that there is no definitive agreement in the literature on the use of some of the terms that signify special cases of Markov processes. Usually the term "Markov chain" is reserved for a process with a discrete set of times, that is, a discrete-time Markov chain DTMC , [1] [17] but a few authors use the term "Markov process" to refer to a continuous-time Markov chain CTMC without explicit mention. Moreover, the time index need not necessarily be real-valued; like with the state space, there are conceivable processes that move through index sets with other mathematical constructs.

Notice that the general state space continuous-time Markov chain is general to such a degree that it has no designated term. While the time parameter is usually discrete, the state space of a Markov chain does not have any generally agreed-on restrictions: the term may refer to a process on an arbitrary state space.

Besides time-index and state-space parameters, there are many other variations, extensions and generalizations see Variations. For simplicity, most of this article concentrates on the discrete-time, discrete state-space case, unless mentioned otherwise. The changes of state of the system are called transitions. The process is characterized by a state space, a transition matrix describing the probabilities of particular transitions, and an initial state or initial distribution across the state space.

By convention, we assume all possible states and transitions have been included in the definition of the process, so there is always a next state, and the process does not terminate. A discrete-time random process involves a system which is in a certain state at each step, with the state changing randomly between steps.

Formally, the steps are the integers or natural numbers , and the random process is a mapping of these to states. Since the system changes randomly, it is generally impossible to predict with certainty the state of a Markov chain at a given point in the future. Markov studied Markov processes in the early 20th century, publishing his first paper on the topic in Other early uses of Markov chains include a diffusion model, introduced by Paul and Tatyana Ehrenfest in , and a branching process, introduced by Francis Galton and Henry William Watson in , preceding the work of Markov.

Andrei Kolmogorov developed in a paper a large part of the early theory of continuous-time Markov processes.

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 [53] or [54]. 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.

A Markov Chain Model for Changes in Users’ Assessment of Search Results

Performed the experiments: JBI. Previous research shows that users tend to change their assessment of search results over time. This is a first study that investigates the factors and reasons for these changes, and describes a stochastic model of user behaviour that may explain these changes. In particular, we hypothesise that most of the changes are local, i. According to the theory of coarse beliefs and categorical thinking, humans tend to divide the range of values under consideration into coarse categories, and are thus able to distinguish only between cross-category values but not within them. To test this hypothesis we conducted five experiments with about subjects divided into 3 groups.


Markov chains are a fundamental class of stochastic processes. They are widely used to solve problems in a large number of domains such as operational.


Modelling manufacturing processes using Markov chains

The joint asymptotic distribution is derived for certain functions of the sample realizations of a Markov chain with denumerably many states, from which the joint asymptotic distribution theory of estimates of the transition probabilities is obtained. Application is made to a goodness of fit test. Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide. Sign In or Create an Account.

The joint asymptotic distribution is derived for certain functions of the sample realizations of a Markov chain with denumerably many states, from which the joint asymptotic distribution theory of estimates of the transition probabilities is obtained. Application is made to a goodness of fit test. Most users should sign in with their email address. If you originally registered with a username please use that to sign in.

Skip to Main Content. A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. Use of this web site signifies your agreement to the terms and conditions. Random-Time, State-Dependent Stochastic Drift for Markov Chains and Application to Stochastic Stabilization Over Erasure Channels Abstract: It is known that state-dependent, multi-step Lyapunov bounds lead to greatly simplified verification theorems for stability for large classes of Markov chain models. In this paper we extend the general theory to randomized multi-step Lyapunov theory to obtain criteria for stability and steady-state performance bounds, such as finite moments.

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Although stochastic process theory and its applications have made great progress in recent years, there are still a lot of new and challenging problems existing in the areas of theory, analysis, and application, which cover the fields of stochastic control, Markov chains, renewal process, actuarial science, and so on. These problems merit further study by using more advanced theories and tools. The aim of this special issue is to publish original research articles that reflect the most recent advances in the theory and applications of stochastic processes. The focus will especially be on applications of stochastic processes as key technologies in various research areas, such as Markov chains, renewal theory, control theory, nonlinear theory, queuing theory, risk theory, communication theory engineering and traffic engineering. Journal overview. Special Issues.

A Markov chain is a stochastic model describing a sequence of possible events in which the probability of each event depends only on the state attained in the previous event. It is named after the Russian mathematician Andrey Markov. Markov chains have many applications as statistical models of real-world processes, [1] [4] [5] [6] such as studying cruise control systems in motor vehicles , queues or lines of customers arriving at an airport, currency exchange rates and animal population dynamics. Markov processes are the basis for general stochastic simulation methods known as Markov chain Monte Carlo , which are used for simulating sampling from complex probability distributions, and have found application in Bayesian statistics , thermodynamics , statistical mechanics , physics , chemistry , economics , finance , signal processing , information theory and artificial intelligence. The adjective Markovian is used to describe something that is related to a Markov process. A Markov process is a stochastic process that satisfies the Markov property [1] sometimes characterized as " memorylessness ".

A Markov Chain Model for Subsurface Characterization: Theory and Applications

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    theory underlying Markov chains and the applications that they have. To this end, we will review some basic, relevant probability theory. Then we will progress to.