convert_Normal_Poisson

Calculate the parameters of the log-Normal that best approximates the given Gamma distribution.
The approximation is the best in the sense that it minimizes the KL divergence from the Gamma to the log-Normal

internal

Provide routines for filtering and smoothing, forecasting, sampling and Bayesian analysis of Dynamic Generalized Linear Models using the methodology described in Alves et al. (2024)<doi:10.48550/arXiv.2201.05387> and dos Santos Jr. et al. (2024)<doi:10.48550/arXiv.2403.13069>.

Silvaneo Vieira dos Santos Junior

kDGLM

Bayesian Analysis of Dynamic Generalized Linear Models

Mariane Branco Alves

Hélio dos Santos Migon

convert_Normal_Poisson function

<dl><dt>conj.param</dt>
<dd>list: A vector containing the parameters of the Gamma (alpha,beta).</dd>
<dt>parms</dt>
<dd>list: A list of extra known parameters of the distribution. Not used in this kernel.</dd></dl>

Arguments

convert_Normal_Poisson — convert_Normal_Poisson

<dl>

<dt>conj.param</dt>
<dd>list: A vector containing the parameters of the Gamma (alpha,beta).</dd>


<dt>parms</dt>
<dd>list: A list of extra known parameters of the distribution. Not used in this kernel.</dd>

</dl>

convert_Normal_Poisson: convert_Normal_Poisson

Description

Usage

Value

Arguments

See Also