In probability, we can find the cdf using the pdf and vise-versa. Integrating pdf yields the cdf.

Does integrating the likelihood function yield any important thing?

In statistics, $\mathcal{L} (M\mid X )= P(X \mid M) $.

If, cdf is to pdf, then likelihood is to what?


Likelihood integration (with respect to $M$) doesn't make sense because it doesn't even integrate/sum to $1$ if we iterate over all possible $M$'s. However, integration/sum of likelihood multiplied with prior makes sense because the integral is equal to the evidence.

With respect to $x$, it's still a density/PMF and its integral is a CDF.


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.