Cited from wikipedia
The likelihood function $L(θ|x)=f(x|θ)$ is not the same as the probability that those parameters are the right ones, given the observed sample. Attempting to interpret the likelihood of a hypothesis given observed evidence as the probability of the hypothesis is a common error, with potentially disastrous real-world consequences in medicine, engineering or jurisprudence. See prosecutor's fallacy for an example of this.
So, is the posterior probability the right ones? Can I think this like posterior probability is just likelihood taking account of prior probability as stated in the Bayes rules?
What confuses more is why maximum-likelihood estimation can be a proper estimate of the model parameters.