36. Diffusion, osmosis. Markov chains are defined by the following procedures at any time n:(a) Bernoulli model. Two adjacent containers A and B each contain m particles; m are of type I andm are of type II. A particle is selected at random in each container. If they are of opposite typesthey are exchanged with probability a if the type I is in A, or with probability ß if the type I is inB. Let Xn be the number of type I particles in A at time n.(b) Ehrenfest dog-flea model. Two adjacent containers contain m particles in all. A particle isselected at random. If it is in A it is moved to B with probability a, if it is in B it is moved to Awith probability B. Let Yn be the number of particles in A at time n.In each case find the transition matrix and stationary distribution of the chain.

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Answered: 36. Diffusion, osmosis. Markov chains…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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The transition matrix for a Markov chain is shown to the right. Find Pk for k= 2, 4, and 8. Can you identify a matrix Q that the matrices pk are approaching? Compute p² p² = 0 (Type an integer or a decimal for each matrix element.) P= A B A B 0.4 0.6 0.3 0.7
Profits at a securities firm are determined by the volume of securities sold, and this volume fluctuates from week to week. If volume is high this week, then next week it will be high with a probability of 0.6 and low with a probability of 0.4. If volume is low this week then it will be high next week with a probability of 0.1. 1. Find the transition matrix for the Markov chain, with high volume being state 1 and low volume state 2. 2. If the volume was low last week, what is the probability the volume will be low this week? 3. If the volume was high this week, what is the probability it will be low three weeks from now? 4. If the volume was high this week, what is the probability it will be alternate between low and high for the next three weeks (i.e. High → Low → High → Low.)
3. A discrete Markov model has state space equal to E {0, 1, 2}. The transition probabilities p are independent of r and recorded in the matrix P defined below. .7 .2 .1 P = .1 .6 .3 1 Assume that Lynn is in state 0 at time 0, and that i = .04. (a.) Determine the probability that Lynn is in state 0 at time 3. (b.) Determine the probability that Lynn transitions from state 1 at time 3 to state 2 at time 4.
A state vector X for a four-state Markov chain is such that the system is four times as likely to be in state 1 as in 3, is not in state 2, and is in state 4 with probability 0.2. Find the state vector X.
If she made the last free throw, then her probability of making the next one is 0.7. On the other hand, If she missed the last free throw, then her probability of making the next one is 0.2. Assume that state 1 is Makes the Free Throw and that state 2 is Misses the Free Throw. (1) Find the transition matrix for this Markov process. %3D
A Markov system with two states satisfies the following rule. If you are in state 1 then of the time you change to state 2. If you are in state 2 then of the time you remain in state 2. state 1 Write the transition matrix for this system using the state vector v state 2 T = Find the long term probability (stable state vector). vs
Suppose it is known that in the city of Golden the weather is either "good" or "bad". If the weather is good on any given day, there is a 2/3 chance it will be good the next day. If the weather is bad on any given day, there is a 1/2 chance it will be bad the next day. a) Find the stochastic matrix P for this Markov chain. b) Given that on Saturday there is a 100% chance of good weather in Golden, use the stochastic matrix from part (a) to find the probability that the weather on Monday will be good. The initial state xo = c) Over the long run, what is the probability that the weather in Golden is good?
Alan and Betty play a series of games with Alan winning each game independently with probability p = 0.6. The overall winner is the first player to win two games in a row. Define a Markov chain to model the above problem.
Consider a random walk on the graph with 6 vertices below. Suppose that 0 and 5 are absorbing vertices, but the random walker is more strongly attracted to 5 than to 0. So for each turn, the probability that the walker moves right is 0.7, while the probability he moves left is only 0.3. (a) Write the transition matrix P for this Markov process. (b) Find POO. (c) What is the probability that a walker starting at vertex 1 is absorbed by vertex 0? (d) What is the probability that a walker starting at vertex 1 is absorbed by vertex 5? (e) What is the probability that a walker starting at vertex 4 is absorbed by vertex 5? (f) What is the probability that a walker starting at vertex 3 is absorbed by vertex 5? What is the expected number of times that a walker starting at vertex 1 will visit vertex 2? (h) What is the expected number of times that a walker starting at vertex 1 will visit vertex 4? N;B The diagram is a horizontal line showing points 0, 1, 2, 3, 4, and 5
A state vector X for a four-state Markov chain is such that the system is three times as likely to be in state 4 as in 3, is not in state 2, and is in state 1 with probability 0.2. Find the state vector X.
A Markov system with two states satisfies the following rule. If you are in state 1 then of the time you change to state 2. If you are in state 2 then of the time you remain in state 2. At time t = 0, there are 100 people in state 1 and no people in the other state. state 1 Write the transition matrix for this system using the state vector v = state 2 T = Write the state vector for time t = 0. Vo Compute the state vectors for time t = 1 and t = 2. Vị = V2
A Markov chain model for a species has four states: State 0 (Lower Risk), State 1 (Vulnerable), State 2 (Threatened), and State 3 (Extinct). For t 2 0, you are given that: 01 zit = 0.03 12 t= 0.05 23 Hit = 0.06 This species is currently in state 0. Calculate the probability this species will be in state 2 ten years later. Assume that reentry is not possible. (Note: This question is similar to #46.2 but with constant forces of mortality) Possīble Answers A 0.02 0.03 0.04 D 0.05 E 0.06

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