# Why is the posterior distribution the same as likelihood function when uniform prior distribution is used in Bayesian Analysis?

While studying bayesian analysis, I am told that the posterior distribution is the same as the likelihood function if we use a uniform prior distribution. I am having some difficulty to understand why it is so. I am referencing a lecture on the Intenet and the link is as follows:

http://www.sumsar.net/blog/2017/02/introduction-to-bayesian-data-analysis-part-one/

The lecturer shows Bayes' Theorem to show the calculation for [pior * likelihood] done in the video but I cannot find when [pior * likelihood] is done in the video. What am I missing here?

The posterior is prior$$\,\times\,$$likelihood$$\,\times\,$$constant; the uniform density is simply a constant and gets absorbed in the other constant term.
Take as an explicit example the prior $$\mathrm{uniform}(0,1)$$; then, since the prior pdf is $$f(\theta) = 1$$, prior$$\,\times\,$$likelihood = 1$$\,\times\,$$likelihood = likelihood.