Exam 16: Markov Processes

Transition probabilities are conditional probabilities.

If the probability of making a transition from a state is 0, then that state is called a(n)

All entries in a row of a matrix of transition probabilities sum to 1.

Markov processes use historical probabilities.

Steady state probabilities are independent of initial state.

State j is an absorbing state if p_ij = 1.

For a situation with weekly dining at either an Italian or Mexican restaurant,

In Markov analysis, we are concerned with the probability that the

If an absorbing state exists, then the probability that a unit will ultimately move into the absorbing state is given by the steady state probability.

A state i is an absorbing state if p_ii = 0.

The probability of reaching an absorbing state is given by the

The sum of the probabilities in a transition matrix equals the number of rows in the matrix.

When absorbing states are present, each row of the transition matrix corresponding to an absorbing state will have a single 1 and all other probabilities will be 0.

All entries in a matrix of transition probabilities sum to 1.

The fundamental matrix is used to calculate the probability of the process moving into each absorbing state.

All Markov chain transition matrices have the same number of rows as columns.

The probability that a system is in a particular state after a large number of periods is

At steady state

A state, i, is an absorbing state if, when i = j, p_ij = 1.

A Markov chain cannot consist of all absorbing states.