How Do You Correctly Express q(y,x) in the Metropolis-Hastings Algorithm?

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The discussion centers on the correct expression of the jump distribution q(y,x) in the Metropolis-Hastings algorithm, specifically when using a normal distribution as the transition probability. The user questions how to express q(y,x) given that their jump distribution is normal(0,1) and highlights confusion regarding the relationship between x and y in the equation. They suggest that the current formulation, which does not involve y, seems incorrect, as q(y,x) should reflect the transition probability from y to x. The conversation emphasizes the importance of correctly relating the variables in the context of the algorithm. Understanding the transition probabilities is crucial for accurate implementation of the Metropolis-Hastings method.
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I digged out this old tread, but it is closed. I'll repost, but with my question.
https://www.physicsforums.com/showthread.php?t=74004&highlight=metropolis

\pi(x)

and
\pi(y)

and
q(y,x) is the jump distribution

in the relation:
\alpha(x,y)= \min \left( 1,\frac{\pi (y)q(y,x)}{\pi (x)q(x,y)} \right)

Say, my jump distribution(aka transition prob) is normal(0,1). How do you write out q(y,x)? Is it \frac{1}{\sqrt{2\pi }\sigma }{{e}^{\frac{{{(x-0)}^{2}}}{2{{\sigma }^{2}}}}}?

But this doesn't make sense, because it doesn't involve y, since q(y,x) means given y, the transition probability of getting x. We are suppose to relate y to x in the equation.
 
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Variables x, y, z, in algebra, are place holders.

q(y,x)=normal(0,1) means y=0 x=1

Algebra is a incomplete story of placeholders. Be careful where you can plugin the values.
 
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