`post = posterior(nb,test)`

[post,cpre] = posterior(nb,test)

[post,cpre,logp] = posterior(nb,test)

[...] = posterior(..., 'HandleMissing',val)

`post = posterior(nb,test)`

returns the posterior
probability of the observations in `test`

according
to the `NaiveBayes`

object `nb`

. `test`

is
a `N`

-by-`nb.ndims`

matrix, where `N`

is
the number of observations in the test data. Rows of `test`

correspond
to points, columns of `test`

correspond to features. `post`

is
a `N`

-by-`nb.nclasses`

matrix containing
the posterior probability of each observation for each class. `post(i,j)`

is
the posterior probability of point I belonging to class `j`

.
Classes are ordered the same as `nb.clevels`

, i.e.,
column `j`

of `post`

corresponds
to the `j`

th class in `nb.clevels`

.
The posterior probabilities corresponding to any empty classes
are `NaN`

.

`[post,cpre] = posterior(nb,test)`

returns `cpre`

,
an `N`

-by-1 vector, containing the class to which
each row of `test`

has been assigned. `cpre`

has
the same type as `nb.CLevels`

.

`[post,cpre,logp] = posterior(nb,test)`

returns `logp`

,
an `N`

-by-1 vector containing estimates of the log
of the probability density function (PDF). `logp(i)`

is
the log of the PDF of point `i`

. The PDF value of
point `i`

is the sum of ```
Prob(point I | class
J) * Pr{class J}
```

taken over all classes.

`[...] = posterior(..., 'HandleMissing',val)`

specifies
how `posterior`

treats NaN (missing values). `val`

can
be one of the following:

`'off'` (default) | Observations with `NaN` in any of the columns
are not classified into any class. The corresponding rows in `post` and `logp` are `NaN` .
The corresponding rows in `cpre` are `NaN` (if
`obj.clevels` is numeric or logical), empty character
vectors (if `obj.clevels` is character array or cell
array of character vectors) or `<undefined>` (if `obj.clevels` is
categorical). |

`'on'` | For observations having `NaN` in some (but
not all) columns, `post` and `cpre` are
computed using the columns with non-`NaN` values.
Corresponding `logp` values are `NaN` . |

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