Solving Sampling Problem: How to Sample from Q(x,y) with Rejection Method?

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To sample from the distribution Q(x,y) using the rejection method, start by selecting a vector from the known distribution W(x,y). After selecting a vector, calculate F(y) based on the chosen y value. Use F(y) as a probability to determine whether to accept or reject the sampled vector; for example, if F(y) is normalized to 0.7, there is a 70% chance of acceptance. This process effectively allows for sampling from Q(x,y) while ensuring the final values align with the desired distribution. The outlined method leverages the rejection sampling technique to achieve the goal.
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I would appreciate if someone could give me some idea about how to solve this sampling problem. I have a distribution W(x,y) of fields x and y characterized by a population of N vectors (x,y). I have to sample x and y fields according to a second distribution Q(x,y) which is related to W by:

Q(x,y) = W(x,y) F(y),

where given some y, I know how to calculate F(y). Now, I can sample from W simply by choosing a vector from my population and then, I can calculate F(y). I am hoping I can use something similar to the rejection method to sample from Q.

Does anyone has some idea about that?

Cheers,
Roberto.
 
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Uhm... I'm not sure I understood what actually IS your problem. Do you have just a bunch of values, or an analytical shape for W(x,y)? Anyway, I think you could do like this:

1) Pick a vector from W(x,y)
2) Calculate F(y)
3) Use it as a probability and make a random sort; if it's "yes", then keep the value; otherwise, reject it and pick another one. Like, if F(y) (normalized) is 0.7, then you have a 70% probability you'll keep the vector.In this way, final values should be distributed following Q(x,y). And it's some sort of rejection method.
 
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