posterior_interval

a matrix containing the posterior samples of a fitted parameter

A number \(p \in (0,1)\) indicating the desired
probability mass to include in the intervals. The default is to report
<code>90%</code> intervals (<code>prob=0.9</code>) rather than the traditionally used
<code>95%</code>.

prob

Further arguments passed to <code>posterior_intervals</code>.

The <code>posterior_interval</code> function computes Bayesian posterior uncertainty
intervals. These intervals are often referred to as credible
intervals, for more details see rstanarm

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

posterior_interval: Posterior uncertainty intervals

Description

Usage

Arguments

Value