Conditional probability table

In statistics, the conditional probability table (CPT) is defined for a set of discrete (not independent) random variables to demonstrate marginal probability of a single variable with respect to the others. For example, assume there are three random variables $x_{1},x_{2},x_{3}$ where each have $K$ states. Then, the conditional probability table of $x_{1}$ provides the marginal probability values for $P(x_{1}\mid x_{2},x_{3})$ . Clearly, this table has K³ cells. In general, for $M$ number of variables $x_{1},x_{2},\ldots ,x_{M}$ with $K$ states, the CPT has size K^M.^[1]

CPT table can be put into a matrix form. For example, the values of $P(x_{j}\mid x_{i})=T_{{ij}}$ create a matrix. This matrix is a stochastic matrix since sum of all its elements is equals to 1; i.e. $\sum _{j}T_{{ij}}$ .

References

↑ Murphy, KP (2012). Machine learning: a probabilistic perspective. The MIT Press.

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