convert_Poisson_Normal

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

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_Poisson_Normal function

<dl><dt>ft</dt>
<dd>numeric: A vector representing the means from the normal distribution.</dd>
<dt>Qt</dt>
<dd>matrix: A matrix representing the covariance matrix of the normal distribution.</dd>
<dt>parms</dt>
<dd>list: A list of extra known parameters of the distribution. Not used in this kernel.</dd></dl>

Arguments

convert_Poisson_Normal — convert_Poisson_Normal

<dl>

<dt>ft</dt>
<dd>numeric: A vector representing the means from the normal distribution.</dd>


<dt>Qt</dt>
<dd>matrix: A matrix representing the covariance matrix of the normal distribution.</dd>


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

</dl>

convert_Poisson_Normal: convert_Poisson_Normal

Description

Usage

Value

Arguments

See Also