Markov Chain


By Prof. Seungchul Lee
http://iai.postech.ac.kr/
Industrial AI Lab at POSTECH

Table of Contents

1. Markov Process (or Chain)

1.1. Sequential Processes

  • Most classifiers ignored the sequential aspects of data
  • Consider a system which can occupy one of $N$ discrete states or categories $q_t \in \{S_1,S_2,\cdots,S_N\}$
  • We are interested in stochastic systems, in which state evolution is random
  • Any joint distribution can be factored into a series of conditional distributions


$$p(q_0,q_1,\cdots,q_T ) = p(q_0) \; p(q_1 \mid q_0) \; p( q_2 \mid q_1 q_0 ) \; p( q_3 \mid q_2 q_1 q_0 ) \cdots$$

Amost impossible to compute !!


$$p(q_0,q_1,\cdots,q_T ) = p(q_0) \; p(q_1 \mid q_0) \; p( q_2 \mid q_1 ) \; p( q_3 \mid q_2 ) \cdots$$

Possible and tractable !!

1.2. Markov Process

  • (Assumption) for a Markov process, the next state depends only on the current state:


$$ p(q_{t+1} \mid q_t,\cdots,q_0) = p(q_{t+1} \mid q_t)$$


  • More clearly


$$ p(q_{t+1} = s_j \mid q_t = s_i) = p(q_{t+1} = s_j \mid q_t = s_i,\; \text{any earlier history})$$


  • Given current state, the past does not matter
  • The state captures all relevant information from the history
  • The state is a sufficient statistic of the future

1.3. State Transition Matrix

  • For a Markov state 𝑠 and successor state 𝑠′, the state transition probability is defined by


$$ P_{ss'} = P\left[S_{t+1} = s' \mid S_t = s \right] $$


  • State transition matrix $P$ defines transition probabilities from all states $s$ to all successor states $s'$.





Example: Markov chhain episodes

  • sample episodes starting from $S_1$
In [1]:
import numpy as np

P = [[0, 0, 1],
    [1/2, 1/2, 0],
    [1/3, 2/3, 0]]
In [2]:
print(P[1][:])

[0.5, 0.5, 0]
In [3]:
a = np.random.choice(3,1,p = P[1][:])
print(a)

[0]
In [4]:
# sequential processes
# sequence generated by Markov chain
# S1 = 0, S2 = 1, S3 = 2

# starting from 0
x = 0
S = []
S.append(x)

for i in range(50):    
    x = np.random.choice(3,1,p = P[x][:])[0]    
    S.append(x)
     
print(S)    

[0, 2, 1, 1, 1, 0, 2, 1, 1, 0, 2, 1, 1, 1, 0, 2, 1, 0, 2, 1, 1, 1, 1, 1, 0, 2, 0, 2, 1, 0, 2, 0, 2, 1, 0, 2, 1, 0, 2, 0, 2, 1, 1, 0, 2, 0, 2, 1, 0, 2, 1]

1.4. Markov Chain Components

It represents passive stochastic behavior

$\;\;$ 1) a finite set of $N$ states, $S = \{ S_1, \cdots , S_N \}$

$\;\;$ 2) a state transition probability, $P = \{ p_{ij} \}_{M \times M},\quad 1 \leq i,j \leq M$

$\;\;$ 3) an initial state probability distribution, $\pi = \{ \pi_i\}$


Example of Markov Chain

  • Starting from $S_1$ = Class 1

In [5]:
P = [[0, 0.5, 0, 0, 0, 0.5, 0],
    [0, 0, 0.8, 0, 0, 0, 0.2],
    [0, 0, 0, 0.6, 0.4, 0, 0],
    [0, 0, 0, 0, 0, 0, 1],
    [0.2, 0.4, 0.4, 0, 0, 0, 0],
    [0.1, 0, 0, 0, 0, 0.9, 0],
    [0, 0, 0, 0, 0, 0, 1]]

# sequential processes
# sequence generated by Markov chain
# [C1 C2 C3 Pass Pub FB Sleep] = [0 1 2 3 4 5 6]

name = ['C1','C2','C3','Pass','Pub','FB','Sleep']

# starting from 0
x = 0 
S = []
S.append(x)

for i in range(5):
    x = np.random.choice(len(P),1,p = P[x][:])[0]    
    S.append(x)
    
print(S)    

[0, 1, 2, 4, 1, 2]
In [6]:
episode = []

for i in S:
    episode.append(name[i])

print(episode)    

['C1', 'C2', 'C3', 'Pub', 'C2', 'C3']

2. Chapman-Kolmogorov Equation

  • (1-step transition probabilities) For a Markov chain on a finite state space, $S = \{ S_1, \cdots , S_N \}$, with transition probability matrix $P$ and initial distribution $\pi = \{ \pi_i^{0} \}$ (row vector) then the distribution of $X(1)$ is given by

  • (2-step transition probabilities) For a Markov chain on a finite state space, $S = \{ S_1, \cdots , S_N \}$, with transition probability matrix $P$ and initial distribution $\pi = \{ \pi_i^{0} \}$ (row vector) then the distribution of $X(2)$ is given by



  • (n-step transition probabilities) For a Markov chain on a finite state space, $S = \{ S_1, \cdots , S_N \}$, with transition probability matrix $P$ and initial distribution $\pi = \{ \pi_i^{0} \}$ (row vector) then the distribution of $X(n)$ is given by



  • $P^n$: n-step transition probabilities
  • Key recursion
$$p_{ij}(n) = \sum_{k=1}^{N} p_{ik}(n-1) p_{kj}(1), \qquad i \rightarrow k \; \text{and}\; k \rightarrow j \;\text{imply}\; i \rightarrow j$$



In [7]:
# state probability distribution

P = [[0, 0, 1],
    [1/2, 1/2, 0],
    [1/3, 2/3, 0]]

u = [0, 1, 0]

P = np.asmatrix(P)
u = np.asmatrix(u)

for i in range(10):
    u = u*P
    print(u) 

[[0.5 0.5 0. ]]
[[0.25 0.25 0.5 ]]
[[0.29166667 0.45833333 0.25      ]]
[[0.3125     0.39583333 0.29166667]]
[[0.29513889 0.39236111 0.3125    ]]
[[0.30034722 0.40451389 0.29513889]]
[[0.30063657 0.3990162  0.30034722]]
[[0.29962384 0.39973958 0.30063657]]
[[0.30008198 0.40029417 0.29962384]]
[[0.3000217  0.39989632 0.30008198]]
In [8]:
u = [0, 1, 0]
u = u*P**10
print(u)

[[0.3000217  0.39989632 0.30008198]]

3. Stationary Distribution

  • Steady-state behavior
  • Does $p_{ij}(n)=P[X_n = j \mid X_0 = i] $ converge to some $\pi_j$ ?
  • Take the limit as $n \rightarrow \infty$
$$p_{ij}(n) = \sum_{k=1}^{N} p_{ik}(n-1) p_{kj}$$$$\pi_j = \sum_{k=1}^{N} \pi_k p_{kj}$$
  • Need also $\sum_j \pi_j = 1$
$$\pi = \pi P$$
In [9]:
# eigenvalue = 1 and associated eigenvector

d, v = np.linalg.eig(P.T)

print(d) # loof for eigenvalue = 1

[-0.25+0.32274861j -0.25-0.32274861j  1.  +0.j        ]
In [10]:
print(v[:,2]/np.sum(v[:,2]))

[[0.3-0.j]
 [0.4-0.j]
 [0.3-0.j]]
In [11]:
%%javascript
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