This is NU's Typepad Profile.
Join Typepad and start following NU's activity
Join Now!
Already a member? Sign In
NU
Recent Activity
It looks like you are using the inverse of the Hessian at the posterior mode as an (initial) proposal covariance. Have you tried scaling this, as in Roberts and Rosenthal, "Examples of adaptive MCMC" (2009)? Not sure if this will help if you're modifying the proposal later. Also, it looks like you can evaluate the Hessian of your posterior at an arbitrary point, not just the mode. Have you considered using a Hessian-exploiting version of Metropolis-Hastings, like stochastic Newton (e.g. arXiv:stat/1502.02008)?
NU is now following The Typepad Team
Dec 3, 2015