Bayesian nonparametric density estimation modeling mixtures by a Ferguson-Klass type algorithm for posterior normalized random measures.

Guillaume Kon Kam King

BNPdensity

Ferguson-Klass Type Algorithm for Posterior Normalized Random
Measures

Julyan Arbel

Ernesto Barrios

Antonio Lijoi

Luis E. Nieto-Barajas

Igor Prünster

dt_ function

<dl><dt>x</dt>
<dd>Numeric vector. Data set to which the density is evaluated.</dd>
<dt>df</dt>
<dd>Numeric constant. Degrees of freedom (&gt; 0, maybe non-integer)</dd>
<dt>mean</dt>
<dd>Numeric constant. Location parameter.</dd>
<dt>sd</dt>
<dd>Positive numeric constant. Scale parameter.
## The function is currently defined as
function(x, df, mean, sd) 
 dt((x - mean) / sd, df, ncp = 0) / sd</dd></dl>

Arguments

Non-standard student-t density — dt_

<dl>

<dt>x</dt>
<dd>Numeric vector. Data set to which the density is evaluated.</dd>


<dt>df</dt>
<dd>Numeric constant. Degrees of freedom (&gt; 0, maybe non-integer)</dd>


<dt>mean</dt>
<dd>Numeric constant. Location parameter.</dd>


<dt>sd</dt>
<dd>Positive numeric constant. Scale parameter.
## The function is currently defined as
function(x, df, mean, sd) 
 dt((x - mean) / sd, df, ncp = 0) / sd</dd>

</dl>

Non-standard student-t density

dt_: Non-standard student-t density

Description

Usage

Arguments

Details