prior_summary.varstan

prior_summary

The function returns a report with the users defined model for the given time series data
and all the current defined priors of the model.

Fit Bayesian time series models using 'Stan' for full Bayesian inference. A wide range
of distributions and models are supported, allowing users to fit Seasonal ARIMA, ARIMAX, Dynamic
Harmonic Regression, GARCH, t-student innovation GARCH models, asymmetric GARCH, Random Walks, stochastic
volatility models for univariate time series. Prior specifications are flexible and explicitly encourage
users to apply prior distributions that actually reflect their beliefs. Model fit can easily be assessed
and compared with typical visualization methods, information criteria such as loglik, AIC, BIC WAIC, Bayes
factor and leave-one-out cross-validation methods. References: Hyndman (2017)
<doi:10.18637/jss.v027.i03>; Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>.

Asael Alonzo Matamoros

bayesforecast

Bayesian Time Series Modeling with Stan

Cristian Cruz Torres

Andres Dala

Rob Hyndman

Mitchell O'Hara-Wild

prior_summary.varstan function

<dl><dt>object</dt>
<dd>a <code>varstan</code> object or one of the defined current defined reports.</dd>
<dt>...</dt>
<dd>additional values need in print methods.</dd></dl>

Arguments

Author

Generic function for extracting information about prior distributions — prior_summary.varstan

<dl>

<dt>object</dt>
<dd>a <code>varstan</code> object or one of the defined current defined reports.</dd>


<dt>...</dt>
<dd>additional values need in print methods.</dd>

</dl>

prior_summary.varstan: Generic function for extracting information about prior distributions

Description

Usage

Value

Arguments

Author

Details

Examples