ttmatrixFromMarginal

Construct tt matrix of a bca from marginal probabilities

Using the Theory of Belief Functions for evidence calculus. Basic probability assignments, or mass functions, can be defined on the subsets of a set of possible values and combined. A mass function can be extended to a larger frame. Marginalization, i.e. reduction to a smaller frame can also be done. These features can be combined to analyze small belief networks and take into account situations where information cannot be satisfactorily described by probability distributions.

Peiyuan Zhu

Using the Theory of Belief Functions

Claude Boivin

ttmatrixFromMarginal function

<dl><dt>marg_probs</dt>
<dd>marginal probabilities</dd>
<dt>from_above</dt>
<dd>=TRUE whether to cut marginal probabilities from above</dd>
<dt>simple</dt>
<dd>=TRUE whether to use simple support function</dd>
<dt>min_prob</dt>
<dd>=0 lower bound on marginal probabilities</dd>
<dt>max_prob</dt>
<dd>=2 upper bound on marginal probabilities</dd></dl>

Arguments

Author

Construct tt matrix of a bca from marginal probabilities — ttmatrixFromMarginal

<dl>

<dt>marg_probs</dt>
<dd>marginal probabilities</dd>


<dt>from_above</dt>
<dd>=TRUE whether to cut marginal probabilities from above</dd>


<dt>simple</dt>
<dd>=TRUE whether to use simple support function</dd>


<dt>min_prob</dt>
<dd>=0 lower bound on marginal probabilities</dd>


<dt>max_prob</dt>
<dd>=2 upper bound on marginal probabilities</dd>

</dl>

ttmatrixFromMarginal: Construct tt matrix of a bca from marginal probabilities

Description

Usage

Value

Arguments

Author

Examples