Show that $X_t=\begin{cases}Y^{(1)}_t&\text{, if }t<\tau_1;\\Y^{(2)}_{t-\tau_1}&\text{, otherwise}\end{cases}$ is Markov, if $Y^{(i)}$ are independent

score 213 · Answer 7 · 2022-05-31 18:30:58

213

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score 117 · Answer 8 · 2026-04-01 14:22:42

117

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score 118 · Answer 9 · 2022-06-01 14:17:00

118

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Ito's integral with respect to translated Brownian motion

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score 47 · Answer 10 · 2022-06-02 11:25:26

47

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score 57 · Answer 11 · 2022-06-03 11:16:37

57

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How can we determine the transition semigroup of the concatenation of independent copies of a Markov process?

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score 36 · Answer 12 · 2022-06-05 18:40:32

36

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When the stationary distribution of $X_{k+1} = f(X_k) + e_k$ is Gaussian?

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score 26 · Answer 13 · 2026-02-24 06:49:57

26

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Reconstruct transition matrix of an irreductible discrete-time markov process

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score 1.6k · Answer 14 · 2026-02-25 07:59:18

1.6k

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Does a Markov chain with Gaussian transitions $p(x_t|x_{t-1})=\mathcal N(\sqrt{1-\beta_t}x_{t-1},\beta_tI)$ tend to $\mathcal N(0,I)$?

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score 104 · Answer 15 · 2026-03-28 10:33:03

104

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Can we determine the transition semigroup of a Markov process by its resolvent?

Published on 28 Mar 2026 - 10:33

List Question

Notation in Kuo's Introduction to Stochastic Integration

Which State Will the Markov Chain Go To Next?

If $Y$ is a Markov process, how can we calculate $\operatorname P\left[Y_{s+t-\sigma};\sigma\le s+t<\tau\in B\mid\mathcal F_s\right]$?

Recurrence of Brownian motion in one-dimension

a quick notational doubt clearing in the definition of Markov chain on uncountable state space

Show that $X_t=\begin{cases}Y^{(1)}_t&\text{, if }t<\tau_1;\\Y^{(2)}_{t-\tau_1}&\text{, otherwise}\end{cases}$ is Markov, if $Y^{(i)}$ are independent

What is the expected number of flips of a fair coin before seeing both $HH$ and $TT$?

Derivative of the variance of a stochastic process

Ito's integral with respect to translated Brownian motion

communicating the idea of stationary distribution and ergodic theorem

How can we determine the transition semigroup of the concatenation of independent copies of a Markov process?

When the stationary distribution of $X_{k+1} = f(X_k) + e_k$ is Gaussian?

Reconstruct transition matrix of an irreductible discrete-time markov process

Does a Markov chain with Gaussian transitions $p(x_t|x_{t-1})=\mathcal N(\sqrt{1-\beta_t}x_{t-1},\beta_tI)$ tend to $\mathcal N(0,I)$?

Can we determine the transition semigroup of a Markov process by its resolvent?

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