# specify_priors: Specify Prior Distributions for SV Models In stochvol: Efficient Bayesian Inference for Stochastic Volatility (SV) Models

## Description

This function gives access to a larger set of prior distributions in case the default choice is unsatisfactory.

## Usage

 ```1 2 3 4 5 6 7 8 9``` ```specify_priors( mu = sv_normal(mean = 0, sd = 100), phi = sv_beta(shape1 = 5, shape2 = 1.5), sigma2 = sv_gamma(shape = 0.5, rate = 0.5), nu = sv_infinity(), rho = sv_constant(0), latent0_variance = "stationary", beta = sv_multinormal(mean = 0, sd = 10000, dim = 1) ) ```

## Arguments

 `mu` one of `sv_normal` or `sv_constant` `phi` one of `sv_beta`, `sv_normal`, or `sv_constant`. If `sv_beta`, then the specified beta distribution is the prior for `(phi+1)/2` `sigma2` one of `sv_gamma`, codesv_inverse_gamma, or `sv_constant` `nu` one of `sv_infinity`, `sv_exponential`, or `sv_constant`. If `sv_exponential`, then the specified exponential distribution is the prior for `nu-2` `rho` one of `sv_beta` or `sv_constant`. If `sv_beta`, then the specified beta distribution is the prior for `(rho+1)/2` `latent0_variance` either the character string `"stationary"` or an `sv_constant` object. If `"stationary"`, then h0 ~ N(`mu`, `sigma^2/(1-phi^2)`). If an `sv_constant` object with value `v`, then h0 ~ N(`mu`, `v`). Here, N(b, B) stands for mean b and variance B `beta` an `sv_multinormal` object

Other priors: `sv_constant()`