The same scenario is used for the remainder of the questions.
A psychiatrist clinic classifies its accounts receivable into
the following four states
State 1. Paid
State 2. Bad debt
State 3. 0-30 days
State 4. 31-90 days
The clinic currently has $8000 accounts receivable in the 0-30 days
state and $2000 in the 31-90 days state. Based on historical
transition from week to week of accounts receivable, the following
matrix of transition probabilities has been developed for the
clinic
pls show work
Plz don't forget to Like
The same scenario is used for the remainder of the questions. A psychiatrist clinic classifies its...
An insurance company classifies its customers into three categories: 1. Good Risk 2. Acceptable Risk 3. Bad Risk Customers independently transition between categories at the end of each year according to a Markov process with the following transition matrix: P = 0.6 0.3 0.1 0.5 0.0 0.5 0.4 0.4 0.2 Find the long-run proportion of time in Good Risk, and the expected number of steps needed to return to Good Risk.
A Markov chain {Xn, n ≥ 0} with state space S = {0, 1, 2} has transition probability matrix 0.1 0.3 0.6 p = 0.5 0.2 0.3 0.4 0.2 0.4 If P(X0 = 0) = P(X0 = 1) = 0.4 and P(X0 = 2) = 0.2, find the distribution of X2 and evaluate P[X2 < X4].
Let Xn be a Markov chain with state space {0, 1, 2}, and transition probability matrix and initial distribution π = (0.2, 0.5, 0.3). Calculate P(X1 = 2) and P(X3 = 2|X0 = 0) 0.3 0.1 0.6 p0.4 0.4 0.2 0.1 0.7 0.2
-1,2,3,4,5,63 and transition matrix Consider a discrete time Markov chain with state space S 0.8 0 0 0.2 0 0 0 0.5 00 0.50 0 0 0.3 0.4 0.2 0.1 0.1 0 0 0.9 0 0 0 0.2 0 0 0.8 0 0.1 0 0.4 0 0 0.5 (a) Draw the transition probability graph associated to this Markov chain. (b) It is known that 1 is a recurrent state. Identify all other recurrent states. (c) How many recurrence classes are...
An absorbing Markov Chain has 5 states where states #1 and #2 are absorbing states and the following transition probabilities are known: p3,2=0.1, p3, 3=0.4, p3,5=0.5 p4,1=0.1, p4,3=0.5, p4,4=0.4 p5,1=0.3, p5,2=0.2, p5,4=0.3, p5,5 = 0.2 (a) Let T denote the transition matrix. Compute T3. Find the probability that if you start in state #3 you will be in state #5 after 3 steps. (b) Compute the matrix N = (I - Q)-1. Find the expected value for the number of...
1.13. Consider the Markov chain with transition matrix: 1 0 0 0.1 0.9 2 0 0 0.6 0.4 3 0.8 0.2 0 0 4 0.4 0.6 0 0 (a) Compute p2. (b) Find the stationary distributions of p and all of the stationary distributions ofp2. (c) Find the limit of p2n(x, x) as n → oo.
(Only need help with parts b and c)
Consider the transition matrix
If the initial state is x(0) = [0.1,0.25,0.65] find the nth
state of x(n). Find the limn→∞x(n)
(1 point) Consider the transition matrix 0.5 0.5 0.5 P 0.3 0.3 0.1 0.2 0.2 0.4 10 a. Find the eigenvalues and corresponding eigenvectors of P. ,-| 0 The eigenvalue λι The eigenvalue λ2-1 The eigenvalue A3 1/5 corresponds to the eigenvector vi <-1,1,0> corresponds to the eigenvector v2 = <2,1,1>...
I'm
trying to solve this differential equations by using matlab and
I've got a plot from the code attached. But I wanna get a plot of
completely sinusoidal form. If I can magnify the plot and expand
x-axis, maybe we can get the sinusoidal form. So help me with this
problem by using matlab. Example is attached in below. One is the
plot from this code and another is example.
function second_order_ode2
t=[0:0.001:1];
initial_x=0;
initial_dxdt=0;
[t,x]=ode45(@rhs,t,[initial_x initial_dxdt]);
plot(t,x(:,1))
xlabel('t')
ylabel('x')...
2. The Markov chain (Xn, n = 0,1, 2, ...) has state space S = {1, 2, 3, 4, 5} and transition matrix (0.2 0.8 0 0 0 0.3 0.7 0 0 0 P= 0 0.3 0.5 0.1 0.1 0.3 0 0.1 0.4 0.2 1 0 0 0 0 1 ) (a) Draw the transition diagram for this Markov chain.
I'm
trying to solve this differential equations by using matlab and
I've got a plot from the code attached. But I wanna get a plot of
completely sinusoidal form. If I can magnify the plot and expand
x-axis, maybe we can get the sinusoidal form. So help me with this
problem by using matlab. Example is attached in below. One is the
plot from this code and another is example.
function second_order_ode2
t=[0:0.001:1];
initial_x=0;
initial_dxdt=0;
[t,x]=ode45(@rhs,t,[initial_x initial_dxdt]);
plot(t,x(:,1))
xlabel('t')
ylabel('x')...