jac.invlogit

<p>value at which to evaluate $J(x)$</p>

<p>Let $X=logit^{-1}(Y)$ be a transformation of a random variable $Y$.  
This function computes the jacobian $J(x)$ when using the density of 
$Y$ to evaluate the density of $X$ via
$$f(x) = f_y(logit(x)) J(x)$$
where 
$$J(x) = d/dx logit(x).$$</p>

Implementation of the 'bisque' strategy for approximate Bayesian posterior inference.  See Hewitt and Hoeting (2019) <arXiv:1904.07270> for complete details.  'bisque' combines conditioning with sparse grid quadrature rules to approximate marginal posterior quantities of hierarchical Bayesian models.  The resulting approximations are computationally efficient for many hierarchical Bayesian models.  The 'bisque' package allows approximate posterior inference for custom models; users only need to specify the conditional densities required for the approximation.

Joshua Hewitt

jac.invlogit: Jacobian for logit transform

Description

Usage

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Examples