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Hierarchical Random Walk (also known as Hierarchical Hidden Markov Model)

Let us consider the following hierarchical (recursive) random walk model, which is also known as the hierarchical hidden Markov model in computer science (https://en.wikipedia.org/wiki/...
Minkov's user avatar
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3 votes
0 answers
494 views

Maximization of a total variation distance subject to another total variation distance in Markov chain

Suppose two dependent random variables $X$ and $V$ from finite alphabets $\mathcal{V}$ and $\mathcal{X}$ with known joint and marginal distributions are given. Let $P_{XV}$ and $P_X$ and $P_V$ are the ...
math-Student's user avatar
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1 vote
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48 views

Sample complexity of estimating a doubly stochastic matrix

Let $P\in\mathbb{R}^{n\times n}$ be a doubly-stochastic matrix. That is: $$P(x,y)\geq 0,\quad \sum_xP(x,y)=1,\quad \sum_yP(x,y)=1.$$ I would like to know if lower and upper bounds on the sample ...
user134977's user avatar
1 vote
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73 views

Reduce the asymptotic variance for a class of Metropolis-Hasting estimates

I'm running the Metropolis-Hastings algorithm with state space $E$, target distribution $\mu=p\lambda$ and proposal kernel $Q$ to estimate $\mu(hf)$ for a fixed function $f:E\to[0,\infty)^3$ and a ...
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1 vote
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56 views

Minimizing the rate of geometric ergodicity of a Metropolis-Hastings kernel depending on a parameter

Let $\tilde\kappa$ denote the transition kernel of the Markov chain generated by the Metropolis-Hastings algorithm with proposal kernel $\tilde Q$ and target distribution $\tilde\mu$. I want to ...
0xbadf00d's user avatar
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1 vote
0 answers
44 views

Validating a probability density distribution forecast model for a Markov process

Let's say we have a Markov process $X_t$, and we come up with a forecast model that takes some information from outside world and says: "value $X_{t+1}$ has probability density distribution $P_t(x)$". ...
mt_christo's user avatar
1 vote
0 answers
101 views

How to fit a stochastic matrix to given data.?

Given a data sequence of noisy observations of a 3-state Markov chain $X$ -- $y_1$,$y_2$,...$y_n$, with two transition matrices $A_1$ and $A_2$ corresponding to different regions (**) in the (unit) ...
inherited_knowledge's user avatar