# Bayesian estimation difference between inferences is conjugate prior and non informative prior [duplicate]

What are the differences in the inferences using non-informative (specifically Jeffrys) priors as compared to the conjugate prior?

$$p(\theta|X) \propto p(X|\theta)\; p(\theta)$$
Moreover, you can have conjugate priors that are "uninformative", for example for binomial likelihood, beta distribution with parameters $$\alpha=\beta=1$$ (uniform), or $$\alpha=\beta=1/2$$ (Jeffreys prior).