One big difference (philosophical basis aside) - in a Bayesian analysis, if you're interested in inference on the parameters, you're normally looking at the posterior distribution, often the posterior mean; ML is looking at the mode of the likelihood. In large samples with uniformative priors, they'll usually be very similar, but in small samples the likelihood can be distinctly asymmetric (and possibly discrete).

One big difference (philosophical basis aside) - in a Bayesian analysis, if you're interested in inference on the parameters, you're normally looking at the posterior distribution, often the posterior mean; ML is looking at the mode of the likelihood. In large samples with uniformative priors, they'll usually be very similar, but in small samples they can be quite different. For example, likelihood can be distinctly asymmetric.

One big difference (philosophical basis aside) - in a Bayesian analysis, if you're interested in inference on the parameters, you're normally looking at the posterior distribution, often the posterior mean; ML is looking at the mode of the likelihood. In large samples with uniformative priors, they'll be very similar, but in small samples the likelihood can be distinctly asymmetric (and possibly discrete).

One big difference (philosophical basis aside) - in a Bayesian analysis, if you're interested in inference on the parameters, you're normally looking at the posterior distribution, often the posterior mean; ML is looking at the mode of the likelihood. In large samples with uniformative priors, they'll usually be very similar, but in small samples the likelihood can be distinctly asymmetric (and possibly discrete).