Maximum Likelihood Estimation with Priors

Context

• BayesiaLab can also take into account Priors when estimating parameters using Maximum Likelihood Estimation.
• Priors reflect any a priori knowledge of an analyst regarding the domain, in other words, expert knowledge. See also Prior Knowledge for Structural Learning.
• These priors are expressed with an analyst-specified, initial Bayesian network (structure and parameters) plus analyst-specified Prior Samples.
• Prior Samples represent the analyst's subjective degree of confidence in the Priors.

$\hat P(X = {x_i}|Pa = p{a_i}) = \frac{{N(X = {x_i},Pa = p{a_i}) + {M_0} \times {P_0}(X = {x_i},Pa = p{a_i})}}{{\sum\nolimits_j {\left( {N(X = {x_j},Pa = p{a_j}) + {M_0} \times {P_0}(X = {x_j},Pa = p{a_i})} \right)} }}$

where

• ${M_0}$ is the degree of confidence in the Prior.
• ${P_0}$ is the joint probability returned by the prior Bayesian network.
• BayesiaLab uses these two terms to generate virtual samples that are subsequently combined with the observed samples from the dataset.

Virtual Database Generator

• With your current Bayesian network, you can generate Priors
• Select Menu > Data > Prior Samples > Generate.
• You can specify ${M_0}$ by setting the number of Prior Samples.

• BayesiaLab uses the current Bayesian network to compute ${P_0}$.

• The existence of a new Virtual Database is indicated by an icon in the lower right corner of the graph window, next to the "real dataset" icon .

• Right-clicking on the Virtual Database icon displays the structure of the prior knowledge that was used for generating the Virtual Samples.

• These Virtual Samples will be combined with the observed "real" samples during the learning process.

Number of Uniform Prior Samples

• Edit Number of Uniform Prior Samples allows you to define prior knowledge in such a way that all the variables are marginally independent (fully unconnected network), and the marginal probability distributions of all nodes are uniform.

• For instance, if the number of Prior Samples is set to 1, one observation ("occurrence") would be "spread across" all states of each node, essentially assigning a "fraction of an observation" to each node's states.

• To apply Smoothed Probability Estimation, select Menu > Edit > Edit Smoothed Probability Estimation

• Specify the number of Prior Samples.