Vector or a <code><a rd-options="" href="/link/ts?package=bssm&version=0.1.8" data-mini-rdoc="bssm::ts">ts</a></code> object of observations.

prior for autoregressive coefficient.

Prior for the standard deviation of noise of the AR-process.

sigma

A fixed value or a prior for the stationary mean of the latent AR(1) process. Parameter is omitted if this is set to 0.

Prior for the standard deviation of observation equation.

sd_y

Prior for the regression coefficients.

beta

xreg

Constructs a simple Gaussian model where the state dynamics follow an AR(1) process.

Efficient methods for Bayesian inference of state space models
via particle Markov chain Monte Carlo and parallel importance sampling type weighted
Markov chain Monte Carlo (Vihola, Helske, and Franks, 2017, <arXiv:1609.02541>).
Gaussian, Poisson, binomial, or negative binomial
observation densities and basic stochastic volatility models with Gaussian state
dynamics, as well as general non-linear Gaussian models and discretised diffusion models
are supported.

Jouni Helske

bssm

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

Matti Vihola

ar1 function

Vector or a <code><a rd-options='' href='ts'>ts</a></code> object of observations.

ar1: Univariate Gaussian model with AR(1) latent process

Description

Usage

Arguments

Value