Markov Chain Statistics

In summary: W5nLCBQUG9zdCBvZiBjb250ZW50LiBJbiBzdWJwb3NlIGEgdGF4aWNhYiBtdm9lcyBhYmxlIHRoZSBhcHBvcnQsIEhvdGVsIEEgYW5kIEhvdGVsIEIgYWNjb3VudCBhZGRpdGlvbiBhbG9uZyBpdCB3aXRoIHRoZSBwdWJsaWMgb2xkIG9mIHRoZSBmb3JtYXRocyB3aXRoIHBhcnRzIE
  • #1
lina29
85
0

Homework Statement


A taxicab moves between the airport, Hotel A, and Hotel B according to a Markov chain with transition probabilities:
P(airport → A) = 0.7,
P(airport → B) = 0.3,
P(A → airport) = 0.9,
P(A → B) = 0.1,
P(B → airport) = 0.8,
P(B → A) = 0.2.
A-If the taxicab starts at the airport, what is the probability that it will be at Hotel A two moves later?
B-Suppose the taxicab starts at the airport with probability 0.6 and starts at Hotel A and Hotel B with probability 0.2 each. What is the probability that it will be at Hotel B two moves later?
C- In the long run, what fraction of visits will the taxicab make to each of the three locations?


Homework Equations





The Attempt at a Solution


I have gotten the answer for parts A & C but I don't understand at all how I would set up the matrix with part B. My initial though was that the matrix was

0 .2 .8
.2 0 .8
.6 .4 0

with the first row/column being hotel A, the second row/column being hotel B, and the third row/column being the airport

then by squaring the matrix I got
.52 .32 .16
.48 .36 .16
.08 .12 .8

and then to get B I added up .32 + .12=.44 which was wrong. What did I do wrong?
 
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  • #2
For (b): in two moves, P{at B} = P{at B|start at port}*P{start at port} + P{at B|start at A}*P{start at A} + P{at B|start at B}*P{start at B}. The conditional probabilities are given by P*P.

RGV
 

1. What is a Markov Chain?

A Markov Chain is a mathematical tool used to model and analyze a sequence of events or states, where the probability of transitioning from one state to another only depends on the current state and not on any previous states. It is a type of stochastic process that is used in various fields, including statistics, physics, biology, and economics.

2. How is a Markov Chain represented?

A Markov Chain is typically represented as a directed graph, with each node representing a state and each edge representing a transition between states. The probability of transitioning from one state to another is typically indicated by the weight of the edge connecting the two states.

3. What is a stationary distribution in Markov Chain?

A stationary distribution in Markov Chain is a probability distribution that remains unchanged after multiple transitions. In other words, if the Markov Chain is left to run for an infinite number of steps, the distribution of states will eventually converge to the stationary distribution regardless of its initial state.

4. What are some applications of Markov Chain?

Markov Chain has numerous applications in various fields. In statistics, it is used for time series analysis, prediction, and modeling of processes that exhibit randomness. In physics, it is used to model systems with random fluctuations. In biology, it is used to study genetic sequences and evolutionary processes. In economics, it is used to study market trends and financial data.

5. What are the limitations of Markov Chain?

While Markov Chain is a useful tool, it has some limitations. One major limitation is that it assumes the future state only depends on the current state and not on any previous states. This may not always be accurate in real-world situations. Additionally, Markov Chain may not be suitable for modeling complex systems with a large number of states. It also requires a considerable amount of data to accurately estimate the transition probabilities between states.

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