Fast Hamiltonian sampling for large scale structure inference

Understand

In this work we present a new and efficient Bayesian method for nonlinear three dimensional large scale structure inference.

• We employ a Hamiltonian Monte Carlo (HMC) sampler to obtain samples from a multivariate highly non-Gaussian lognormal Poissonian density posterior given a set of observations.
• The HMC allows us to take into account the nonlinear relations between the observations and the underlying density field which we seek to recover.
• As the HMC provides a sampled representation of the density posterior any desired statistical summary, such as the mean, mode or variance, can be calculated from the set of samples.