1
vote
2answers
49 views

Approximating Probability Distribution by Sampling

Consider a discrete probability distribution over $n$ events. Assume that the probabilistic kernel is a black box, that is, we can only sample from it without knowing anything about the type or ...
0
votes
0answers
42 views

Nice conditional distribution / Closure under noisy observation

Let $X, Y, Z$ be Polish spaces; $M$ a collection of full-support Borel measures on $X$; $\nu$ a Borel measure on $Y$; $f:X\times Y \to Z$ continuous with the property that $f(\cdot,y)$ is injective ...
1
vote
2answers
243 views

A sampling and learning question

Suppose there is an oracle that returns a number $b \in \mathbb{Z}_{n}$ whenever I press the button. We have $b = a + e$, where $a \in \mathbb{Z}_n$ is a fixed number and $e$ is sampled according to ...
2
votes
2answers
309 views

Sampling without replacement until hitting a subset

I randomly sample uniformly from $ \{1,..,N \}$ without replacement until drawing a number $ \leq k$. Denote the expected number of draws by $R(N,k)$. I want a good approximation for $\sum_{k=1}^N ...
3
votes
1answer
377 views

When can you describe a population and its component subpopulations with the same parametric family of distributions?

I believe that it is often the case that you are trying to select the best probability distribution to use to describe some phenomenon you are studying, and you have data not only for a population, ...