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        <td><h2><font color="#FFFFFF">Inside the Estimate Box</font></h2></td>
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<p>An Estimate box in the main workspace looks like this:
<p><img src="../../images/im_highlight.gif" width="276" height="67"></p>
<p><br>
<p><img
        src="../../images/Uestimsate.jpg" alt="" width="498" height="136" style="width: 498px; height: 136px;"><br>
    <br>
    The Estimate program takes a parametrized model (in PM) and a data set
    for the variables in that model, and returns an Instantiated Model,
    an IM. It will also take
    data and an (ML) IM as input.. Once a model is estimated, the contents
    of
    the Estimate box
    can be transferred to an empty IM box&nbsp; and then used to generate
    data, to classify, or to update (in the
    last two cases, only if the model is a Bayes net, not a SEM). <br>
    <br>
    If a Maximum Likelihood Bayes Net and data are directly connected to
    Estimate, the estimation procedures will ignore all cases in the data
    set with missing values for any&nbsp; variables. Missing data values
    can be interpolated by connecing the data to a Manipulate Data box, and
    connecting that box to the Estimator box.<br>
    <br>
    There are several&nbsp; varieties of estimation, depending on
    the.graphical input (the PM or IM):<br>
    <br>
    1. If the input PM&nbsp; or IM&nbsp; is for a SEM, the Estimate program
    immediately produces a full
    information maximum likelihood estimate of the parameters, provided the
    model in the PM or IM is identifiable. Latent variables are allowed.
    The procedure also gives
    model statistics, including
    the implied covariance and correlation matrices, and the chi square
    likelihood ratio statistic and its p value for the model.<br>
    <br>
    2. If the input is a PM for a Bayes Net,&nbsp; the Estimate program
    produces a maximum likelihood estimate of the model parameters,
    provided the model has no latent variables..<br>
    <br>
    3. If the input is an Maximum Likelihhod Instatiated Bayes Net (an IM),
    the Estimate program produces a maximum likelihood estimate of the
    model parameters.<br>
    <br>
    <span style="font-weight: bold;"></span>4. If the input is a Dirichlet
    Instantiated Bayes Model,&nbsp; the Dirichlet Bayes estimator estimates
    a posterior Dirichlet Bayes
    instantiated model given a prior Dirichlet Bayes instantiated model and
    a a discrete data set. The data set must contain all of the same
    variables as the prior instantiated model.
    Latent variables are not allowed.<br>
    <br>
    The Dirichlet estimation algorithm is simple. First, a new (blank)
    posterior
    Dirichlet Bayes IM is created. Then, for each cell in the posterior,
    the value (a) from the corresponding cell in the prior is retrieved,
    and the number of cases in the data satisfying the condition of that
    cell (n) is counted. The value of the cell in the posterior is set to a
    + n. Estimated conditional probabilities total pseudocount in each row
    are calculated from these cell values. <br>
    <br>
    As a shortcut, it is possible in the interface to use a Bayes PM and a
    discrete data set as parents to the Dirichlet Bayes Estimator. If you
    do this, a symmetric Dirichlet Bayes IM will be generated in the
    background and used as the prior for the Dirichlet estimation
    algorithm. The symmetric pseudocount that should be used here may be
    specified at time of construction. <br>
    <br>
    In its present implementation, <span style="font-weight: bold;">Bayes
  nets with
latent variables cannot be estimated. </span></p>
<p>&nbsp;</p>
<p>Types of estimators: </p>
<ul>
    <li><a href="ml_bayes_estimator.html">ML Bayes Estimator</a></li>
    <li><a href="sem_estimator.html">SEM Estimator</a></li>
    <li><a href="dirichlet_estimator.html">Dirichlet Estimator</a> <br>
        <br>
    </li>
</ul>
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