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Markov Chains 1.

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Presentation on theme: "Markov Chains 1."— Presentation transcript:

1 Markov Chains 1

2 Markov Chains (1) A Markov chain is a mathematical model for stochastic systems whose states, discrete or continuous, are governed by transition probability. Suppose the random variable take state space (Ω) that is a countable set of value. A Markov chain is a process that corresponds to the network. 2

3 Markov Chains (2) The current state in Markov chain only depends on the most recent previous states. Transition probability where 3

4 An Example of Markov Chains
where is initial state and so on. is transition matrix. 4

5 Definition (1) Define the probability of going from state i to state j in n time steps as A state j is accessible from state i if there are n time steps such that , where A state i is said to communicate with state j (denote: ), if it is true that both i is accessible from j and that j is accessible from i. 5

6 Definition (2) A state i has period if any return to state i must occur in multiples of time steps. Formally, the period of a state is defined as If , then the state is said to be aperiodic; otherwise ( ), the state is said to be periodic with period 6

7 Definition (3) A set of states C is a communicating class if every pair of states in C communicates with each other. Every state in a communicating class must have the same period Example: 7

8 Definition (4) A finite Markov chain is said to be irreducible if its state space (Ω) is a communicating class; this means that, in an irreducible Markov chain, it is possible to get to any state from any state. Example: 8

9 Definition (5) A finite state irreducible Markov chain is said to be ergodic if its states are aperiodic Example: 9

10 Definition (6) A state i is said to be transient if, given that we start in state i, there is a non-zero probability that we will never return back to i. Formally, let the random variable Ti be the next return time to state i (the “hitting time”): Then, state i is transient iff there exists a finite Ti such that: 10

11 Definition (7) A state i is said to be recurrent or persistent iff there exists a finite Ti such that: The mean recurrent time State i is positive recurrent if is finite; otherwise, state i is null recurrent. A state i is said to be ergodic if it is aperiodic and positive recurrent. If all states in a Markov chain are ergodic, then the chain is said to be ergodic. 11

12 Stationary Distributions
Theorem: If a Markov Chain is irreducible and aperiodic, then Theorem: If a Markov chain is irreducible and aperiodic, then and where is stationary distribution. 12

13 Definition (8) A Markov chain is said to be reversible, if there is a stationary distribution such that Theorem: if a Markov chain is reversible, then 13

14 An Example of Stationary Distributions
A Markov chain: The stationary distribution is 2 1 3 0.4 0.3 0.7 14

15 Properties of Stationary Distributions
Regardless of the starting point, the process of irreducible and aperiodic Markov chains will converge to a stationary distribution. The rate of converge depends on properties of the transition probability. 15

16 Monte Carlo Markov Chains
16

17 Monte Carlo Markov Chains
MCMC method are a class of algorithms for sampling from probability distributions based on constructing a Markov chain that has the desired distribution as its stationary distribution. The state of the chain after a large number of steps is then used as a sample from the desired distribution. 17

18 Metropolis-Hastings Algorithm

19 Metropolis-Hastings Algorithm (1)
The Metropolis-Hastings algorithm can draw samples from any probability distribution , requiring only that a function proportional to the density can be calculated at . Process in three steps: Set up a Markov chain; Run the chain until stationary; Estimate with Monte Carlo methods.

20 Metropolis-Hastings Algorithm (2)
Let be a probability density (or mass) function (pdf or pmf). is any function and we want to estimate Construct the transition matrix of an irreducible Markov chain with states , where and is its unique stationary distribution.

21 Metropolis-Hastings Algorithm (3)
Run this Markov chain for times and calculate the Monte Carlo sum then Sheldon M. Ross(1997). Proposition 4.3. Introduction to Probability Model. 7th ed.

22 Metropolis-Hastings Algorithm (4)
In order to perform this method for a given distribution , we must construct a Markov chain transition matrix with as its stationary distribution, i.e Consider the matrix was made to satisfy the reversibility condition that for all and . The property ensures that for all and hence is a stationary distribution for

23 Metropolis-Hastings Algorithm (5)
Let a proposal be irreducible where , and range of is equal to range of . But is not have to a stationary distribution of . Process: Tweak to yield . States from Qij not π Tweak States from Pij π

24 Metropolis-Hastings Algorithm (6)
We assume that has the form where is called accepted probability, i.e. given , take

25 Metropolis-Hastings Algorithm (7)
For WLOG for some , In order to achieve equality (*), one can introduce a probability on the left-hand side and set on the right-hand side.

26 Metropolis-Hastings Algorithm (8)
Then These arguments imply that the accepted probability must be

27 Metropolis-Hastings Algorithm (9)
M-H Algorithm: Step 1: Choose an irreducible Markov chain transition matrix with transition probability . Step 2: Let and initialize from states in . Step 3 (Proposal Step): Given , sample form .

28 Metropolis-Hastings Algorithm (10)
M-H Algorithm (cont.): Step 4 (Acceptance Step): Generate a random number from If , set else Step 5: , repeat Step 3~5 until convergence.


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