Marginals {BayesNetBP} | R Documentation |

## Obtain marginal distributions

### Description

Get the marginal distributions of multiple variables

### Usage

```
Marginals(tree, vars)
```

### Arguments

`tree` |
a |

`vars` |
a |

### Details

Get the marginal distributions of multiple variables. The function `Marginals`

returns a `list`

of marginal distributions. The marginal distribution of a discrete variable
is a named vector of probabilities. Meanwhile, the marginal distributions of
continous variables in a CG-BN model are mixtures of Gaussian distributions.
To fully represent this information, the marginal of a continuous variable is represented by
a `data.frame`

with three columns to specify
parameters for each Gaussian distribution in the mixture, which are

`mean`

the mean value of a Gaussian distribution.

`sd`

the standard deviation of a Gaussian distribution.

`n`

the number of Gaussian mixtures

### Value

`marginals`

a

`list`

of marginal distributions`types`

a named

`vector`

indicating the types of the variables whose marginals are queried:`TRUE`

for discrete,`FALSE`

for continuous.

### Author(s)

Han Yu

### 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

`PlotMarginals`

for visualization of the marginal distributions,
`SummaryMarginals`

for summarization of the marginal distributions of
continuous variables.

### Examples

```
data(liver)
tree.init.p <- Initializer(dag=liver$dag, data=liver$data,
node.class=liver$node.class,
propagate = TRUE)
tree.post <- AbsorbEvidence(tree.init.p, c("Nr1i3", "chr1_42.65"), list(1,"1"))
marg <- Marginals(tree.post, c("HDL", "Ppap2a"))
marg$marginals$HDL
head(marg$marginals$Ppap2a)
```

*BayesNetBP*version 1.6.1 Index]