Explain the difference between bayesian estimate and maximum likelihood estimation (mle)?
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Answer / Sonal Agarwal
Maximum Likelihood Estimation (MLE) is a method for estimating parameters of a statistical model by finding the values that maximize the likelihood function. It assumes a parametric model and finds the parameter values that best fit the observed data. Bayesian Estimate, on the other hand, is a method that estimates parameters based on both prior knowledge about the parameters and the observed data. It involves specifying a prior distribution for the parameters and updating this distribution based on the likelihood of the observed data.
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