– Stochastic Course Notes
Monthly Archives: December 2014
Markov Chain — Branching Process
– Stochastic Course Notes
1. Definition
Consider a population
- Each individual produces j new offspring each period with probability . Key assumption: This distribution is constant over time.
- Assume that for all j (i.e., the problem is not deterministic)
- Let be the size of the population at period n
2. Number of Population in the n-th Generation
3. Probability of Die Out
4. Example: Family Genetics
Problem:
Markov Chain — Tendem Queue
Markov Chain – Classifications of States
1. Definition of States
- Communicate: State i and j communicate () if i is reachable from j and j is reachable from i. (Note: a state i always communicates with iteself)
- Irreducible: a Markov chains is irreducible if all states are in the same communication class.
- Absorbing state:
- Closed Set: a set of states S is a closed set if no state outside of S is reachable from any state in S. (This is like absorbing set of states)
- : be the probability that, starting in state i, the process returns (at some point) to the sate i
- Transient: a state is transient if
- Recurrent: a state that is not transient is recurrent, i.e., . There are two types of reurrent states
- Positive recurrent: if the expected time to return to the state if finite
- Null recurrent: if the expected time to return to the sate i is infinite (this requires an infinite number of states)
- Periodic: a state is i period if where k is the smallest number such that all paths leading from state i back to state i has a multiple of k transitions
- Aperiodic: if it has period k =1
- Ergodic: a state is ergodic if it is positive recurrent and aperiodic.
2. Example: Gambler’s Ruin
Markov Chain (Continuous Time)
Markov Chain (Discrete Time)
1. Definition
Let be a stochastic process, taking on a finite or countable number of values.
is a DTMC if it has the Markov property: Given the present, the future is independent of the past
We define , since has the stationary transition probabilities, this probability is not depend on n.
Transition probabilities satisfy
2. n Step transition Probabilities
Proof:
3. Example: Coin Flips
4. Limiting Probabilities
Theorem: For an irreducible, ergodic Markov Chain, exists and is independent of the starting state i. Then is the unique solution of and .
Two interpretation for
- The probability of being in state i a long time into the future (large n)
- The long-run fraction of time in state i
- If Markov Chain is irreducible and ergodic, then interpretation 1 and 2 are equivalent
- Otherwise, is still the solution to , but only interpretation 2 is valid.
Splitting a Poisson Process: M/G/Infinity Queue
1. Notation
- M: “Markovian” or “Memoryless” arrival process (i.e., Poisson Process)
- G: General service time (not necessarily exponential)
- : Infinite number of servers
- be the number of customers who have completed service by time t
- be the number of customers who are being served at time t
- be the total number of customers who have arrived by time t
2. Splitting the arrival process
- Fix a reference time T.
- Consider the process of customers arriving prior to time T.
- A customer arriving at time is
- Type I: if service is completed before T
- occur with probability
- Type-II: if customer still is service at time T
- occur with probability
- is a Poisson random variable with mean .
- is a Poisson random variable with mean
- and are independent
- for large t. Therefore, is a Poisson random variable with mean
- is a Poisson random variable with mean
3. Example
Compound Poisson Process (CPP)
1. Definition:
Remove the restriction that two or more customers cannot arrive at the same time, (i.e., remove orderliness property)
2. Expectation:
3. Variance:
Non-Homogeneous Poisson Process (NPHH)
1. Properties:
- N(0) = 0
- N(t) has independent increments
- This is like a Poisson process, without the stationary assumption
2. Definition:
The mean value function (for a NHPP) is
3. Key Properties:
- For a NHPP, N(s+t) – N(s) is a Poisson random variable with mean
The Poisson Process
1. Priliminary Definitions
Def : A stochastic process is a collection of random variable (RV) indexed by time .
- If T is continuous set, the process is a continuous time stochastic process (e.g., Poisson Process)
- If T is countable, then the process is a discrete time stochastic process (e.g., Markov chain)
- (that is, N(t) is non-negative integer)
- If , the ( that is, N(t) is non-decreasing in t)
- For , is the number of events occurring in the time interval .
2. Definition of Poisson Process
Definition 1: A Poisson process is a counting process with rate , if:
- N(0) = 0;
- The Process has independent increments
- The number of events in any interval of length t is a Poisson RV with mean .
Definition 2: A Poisson process is a counting process with rate if
- N(0) = 0
- The process has stationary increments
- The process has independent increments
- (# of events approximately proportional to the length of interval)
- (can’t have 2 or more events at the same time — “orderliness”)
- Eliminate assumption 2, Non-stationary Poisson Process
- Eliminate assumption 3, Mixture of Poisson Process (choose randomly, then run a Poisson process)
- Eliminate assumption 5, compound Poisson Process
3. Conditional Distribution of Event Times