So I read this post Why is the posterior distribution the same as the likelihood function when uniform prior distribution is used in Bayesian Analysis, and learned that when we have a uniform prior, the posterior distribution is the same as the likelihood function. However, we also have that What is the reason that a likelihood function is not a pdf, i.e. likelihood is not a pdf. For example, the sum of the likelihood might not be 1.
Given these, how should we understand the posterior distribution is the same as the likelihood function when we have a uniform prior? Does this mean the sum of a posterior distribution doesn't need to be 1?