cpp_example_model

Example C++ Codes for Non-Linear and SDE Models

Efficient methods for Bayesian inference of state space models
via Markov chain Monte Carlo (MCMC) based on parallel
importance sampling type weighted estimators
(Vihola, Helske, and Franks, 2020, <doi:10.1111/sjos.12492>),
particle MCMC, and its delayed acceptance version.
Gaussian, Poisson, binomial, negative binomial, and Gamma
observation densities and basic stochastic volatility models
with linear-Gaussian state dynamics, as well as general non-linear Gaussian
models and discretised diffusion models are supported.
See Helske and Vihola (2021, <doi:10.32614/RJ-2021-103>) for details.

Jouni Helske

bssm

Bayesian Inference of Non-Linear and Non-Gaussian State Space
Models

Matti Vihola

cpp_example_model function

<dl><dt>example</dt>
<dd>Name of the example model.
Run <code>cpp_example_model("abc")</code> to get the names of possible models.</dd>
<dt>return_code</dt>
<dd>If TRUE, will not compile the model but only returns the
corresponding code.</dd></dl>

Arguments

Example C++ Codes for Non-Linear and SDE Models — cpp_example_model

<dl>

<dt>example</dt>
<dd>Name of the example model.
Run <code>cpp_example_model("abc")</code> to get the names of possible models.</dd>


<dt>return_code</dt>
<dd>If TRUE, will not compile the model but only returns the
corresponding code.</dd>

</dl>

cpp_example_model: Example C++ Codes for Non-Linear and SDE Models

Description

Usage

Value

Arguments

Examples