Bayesian Methods of Parameter Estimation

The frequentest approach is the classical approach to parameter estimation. It assumes that there is an unknown but objectively fixed parameter θ [3]. It chooses the value of θ which maximizes the likelihood of observed data [4], in other words, making the available data as likely as possible. A common example is the maximum likelihood estimator (MLE). The frequentest approach is statistically driven, and defines probability as the frequency of successful trials over the number of total trials in an experiment. For example, in a coin toss experiment, we toss the coin 100 times and it comes out 25 times as heads and 75 times as tails. The probabilities are extracted directly from the given data as: (P = heads) = 1/4 and (P = tails) = 3/4.