I am reading the Wikipedia article on posterior probability and I note the expression:
$$P(\theta|X) = \frac{P(X|\theta)P(\theta)}{P(X)}$$
I understand that $\theta$ represents the parameters of the probability model underlying $P$. However, what is actually meant by $P(\theta)$ and how would I calculate this?
In some sense, when I am analyzing data relative to a model, aren't the parameters under my control? And if so, wouldn't I use something like a uniform distribution for $P(\theta)$? If the parameters are continuous, wouldn't this value be 0? How do I connect $P(\theta)$ to ground truth?