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world:coupling [2024/06/14 23:31] rdouc [A new coupling technique] |
world:coupling [2024/06/20 15:02] (current) rdouc ↷ Page moved from yazid:coupling to world:coupling |
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We draw jointly the couple of random variables (˜Y,Y) according to the following procedure: | We draw jointly the couple of random variables (˜Y,Y) according to the following procedure: | ||
- | * Draw ˜Y∼˜π | + | * Draw ˜Y∼˜π. |
- | * Draw a candidate X∼π and accept the candidate Y=X with probability 1 -\alpha(X,\tilde Y) with \alpha(x,y)=\frac{\pi(y) \tilde \pi(x)}{\pi(x) \tilde \pi(y)} \wedge 1. Otherwise reject the candidate and set Y=\tilde Y. | + | * Draw X \sim \pi and set Y=X with probability 1 -\alpha(X,\tilde Y) where \alpha(x,y)=\frac{\pi(y) \tilde \pi(x)}{\pi(x) \tilde \pi(y)} \wedge 1. Otherwise set Y=\tilde Y. |
<WRAP center round tip 80%> **Proposition. ** (\tilde Y,Y) is a coupling of (\tilde \pi,\pi). | <WRAP center round tip 80%> **Proposition. ** (\tilde Y,Y) is a coupling of (\tilde \pi,\pi). | ||
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<WRAP center round important 80%> | <WRAP center round important 80%> | ||
- | What is nice is that we are able to couple these random variables whereas their densities are known only up to a multiplicative constant. I wonder if it is better to couple in that way: \pi(y) \propto Q(x,\rmd y) \alpha_{MH}(x,y) and \tilde \pi(y) \propto Q(x',\rmd y) \alpha_{MH}(x',y). These two densities are known only up to multiplicative constant. Up to some tricks, we can deduce a way of coupling two MH starting from different initial distributions? Can we compare it to the coupling of Pierre Jacob et al.? | + | * What is nice is that we are able to couple these random variables whereas their densities are known only up to a multiplicative constant. I wonder if it is interesting to couple in that way: \pi(y) \propto Q(x,\rmd y) \alpha_{MH}(x,y) and \tilde \pi(y) \propto Q(x',\rmd y) \alpha_{MH}(x',y). Up to some tricks, can we deduce a way of coupling two MH starting from different initial distributions, maybe with delayed coupling? Can we compare it to the coupling of Pierre Jacob et al.? |
+ | * Moreover, if we look at the problem today, if (\tilde X_0,X_0)=\tilde \pi \otimes \pi then, (\tilde X_1,X_1) is a coupling of (\tilde \pi,\pi), no? | ||
</WRAP> | </WRAP> | ||
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- | Indeed, write: | + | Indeed, write, using the detailed balance condition in the second line : |
\begin{align*} | \begin{align*} | ||
\int \tilde \pi(\rmd \tilde y) K(\tilde y,\rmd y) f(y)&= \int \pi(\rmd y) f(y) \lrcb{\int \lrb{1-\alpha(y,\tilde y)}\tilde \pi(\rmd \tilde y)} + \int \tilde \pi(y) f(y)\rmd y \int \pi(x) \alpha(x,y) \rmd x \\ | \int \tilde \pi(\rmd \tilde y) K(\tilde y,\rmd y) f(y)&= \int \pi(\rmd y) f(y) \lrcb{\int \lrb{1-\alpha(y,\tilde y)}\tilde \pi(\rmd \tilde y)} + \int \tilde \pi(y) f(y)\rmd y \int \pi(x) \alpha(x,y) \rmd x \\ | ||
& = \int \pi(\rmd y) f(y) \lrcb{\int \lrb{1-\alpha(y,\tilde y)}\tilde \pi(\rmd \tilde y)} + \int f(y) \rmd y \lrcb{\int \underbrace{\tilde \pi(y) \pi(x) \alpha(x,y)}_{\tilde \pi(x) \pi(y) \alpha(y,x)} \rmd x} \\ | & = \int \pi(\rmd y) f(y) \lrcb{\int \lrb{1-\alpha(y,\tilde y)}\tilde \pi(\rmd \tilde y)} + \int f(y) \rmd y \lrcb{\int \underbrace{\tilde \pi(y) \pi(x) \alpha(x,y)}_{\tilde \pi(x) \pi(y) \alpha(y,x)} \rmd x} \\ | ||
& = \int \pi(\rmd y) f(y) \lrcb{\int \lrb{1-\alpha(y,\tilde y)}\tilde \pi(\rmd \tilde y)} + \int f(y) \rmd y \lrcb{\int \tilde \pi(x) \pi(y) \alpha(y,x) \rmd x}\\ | & = \int \pi(\rmd y) f(y) \lrcb{\int \lrb{1-\alpha(y,\tilde y)}\tilde \pi(\rmd \tilde y)} + \int f(y) \rmd y \lrcb{\int \tilde \pi(x) \pi(y) \alpha(y,x) \rmd x}\\ | ||
- | & = \int \pi(\rmd y) f(y) \lrcb{\int \lrb{1-\alpha(y,\tilde y)}\tilde \pi(\rmd \tilde y)} + \int f(y) \pi(\rmd y) \lrcb{\int \alpha(y,x) \tilde \pi(\rmd x)} \\ | + | & = \int \pi(\rmd y) f(y) \lrcb{1-\int \alpha(y,\tilde y)\tilde \pi(\rmd \tilde y)} + \int f(y) \pi(\rmd y) \lrcb{\int \alpha(y,x) \tilde \pi(\rmd x)} \\ |
& = \pi(f) | & = \pi(f) | ||
\end{align*} | \end{align*} | ||
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- | The probability of coupling is given by: | + | The coupling probability is given by: |
$$ | $$ | ||
- | \PP(\tilde Y=Y)=\int \tilde \pi(\rmd \tilde y) \pi(\rmd y) \alpha (y,\tilde y)=\int \lrb{\pi(x) \tilde \pi(\tilde y) \wedge \pi(\tilde y) \tilde \pi(x)} \rmd x \rmd \tilde y | + | \PP(\tilde Y=Y)=\int \tilde \pi(\rmd \tilde y) \pi(\rmd x) \alpha (x,\tilde y)=\int \lrb{\pi(x) \tilde \pi(\tilde y) \wedge \pi(\tilde y) \tilde \pi(x)} \rmd x \rmd \tilde y |
$$ | $$ | ||
<WRAP center round tip 80%> | <WRAP center round tip 80%> | ||
- | **Question**: we know that $\PP(\tilde Y=Y) \leq \int \pi(x) \wedge \tilde \pi(\tilde x) \rmd x$. But I can't see how to prove | + | **Question**: we know that \PP(\tilde Y=Y) \leq \int \pi(x) \wedge \tilde \pi(x) \rmd x. But I can't see how to prove |
$$ | $$ | ||
\int \lrb{\pi(x) \tilde \pi(\tilde y) \wedge \pi(\tilde y) \tilde \pi(x)} \rmd x \rmd \tilde y \leq \int \pi(x) \wedge \tilde \pi(\tilde x) \rmd x | \int \lrb{\pi(x) \tilde \pi(\tilde y) \wedge \pi(\tilde y) \tilde \pi(x)} \rmd x \rmd \tilde y \leq \int \pi(x) \wedge \tilde \pi(\tilde x) \rmd x |