SummaryMarginals {BayesNetBP} | R Documentation |

## Summary a continuous marginal distribution

### Description

This function summary the marginal distributions of continuous variables by outputing the mean, standard deviation, and number of subpopulations

### Usage

```
SummaryMarginals(marginals)
```

### Arguments

`marginals` |
the marginal distributions obtained from |

### Value

a `data.frame`

object containing information about the marginal distributions for continuous variables.
The marginal distributions of continous variables in a CG-BN model are mixtures of Gaussian distributions.
Therefore, besides the mean and standard deviation, the object has an additional column to specify the number of Gaussian
mixtures.

`mean`

the mean value of a Gaussian distribution.

`sd`

the standard deviation of a Gaussian distribution.

`n`

the number of Gaussian distributions in the mixture.

### References

Cowell, R. G. (2005). Local propagation in conditional Gaussian Bayesian networks.
Journal of Machine Learning Research, 6(Sep), 1517-1550.

Yu H, Moharil J, Blair RH (2020). BayesNetBP: An R Package for Probabilistic Reasoning in Bayesian
Networks. Journal of Statistical Software, 94(3), 1-31. <doi:10.18637/jss.v094.i03>.

### See Also

### Examples

```
data(liver)
tree.init.p <- Initializer(dag=liver$dag, data=liver$data,
node.class=liver$node.class,
propagate = TRUE)
marg <- Marginals(tree.init.p, c("HDL", "Ppap2a", "Neu1"))
SummaryMarginals(marginals=marg)
```

*BayesNetBP*version 1.6.1 Index]