od of the observed data. However, it can overfit the data, especially when dealing with small datasets.</p>
<p>While very straightforward, the ML estimator has the problem of <em>overfitting</em> to the data. If the observed data is not representative for the underlying distribution, ML estimations will be extremely far off. When estimating parameters for Bayesian networks, lack of data is a frequent problem. Even if the total sample size is very large, the fact that state counts are done conditionally for 