Cosmic shear measurement with maximum likelihood and maximum a posteriori inference

A maximum-likelihood galaxy shear measurement code for cosmic gravitational lensing

We present and describe IM3SHAPE, a new publicly available galaxy shape measurement code for weak gravitational lensing shear. IM3SHAPE performs a maximum likelihood fit of a bulgeplus-disc galaxy model to noisy images, incorporating an applied point spread function. We detail challenges faced and choices made in its design and implementation, and then discuss various limitations that affect th...

Batch Maximum Likelihood (ML) and Maximum A Posteriori (MAP) Estimation With Process Noise for Tracking Applications

Batch maximum likelihood (ML) and maximum a posteriori (MAP) estimation with process noise is now more than thirty-five years old, and its use in multiple target tracking has long been considered to be too computationally intensive for real-time applications. While this may still be true for general usage, it is ideally suited for special needs such as bias estimation, track initiation and spaw...

High Dimensional Inference with Random Maximum A-Posteriori Perturbations

In this work we present a new approach for high-dimensional statistical inference that is based on optimization and random perturbations. This framework injects randomness to maximum a-posteriori (MAP) predictors by randomly perturbing its potential function. When the perturbations are of low dimension, sampling the perturb-max prediction is as efficient as MAP optimization. A classic result fr...

Approximate Maximum A Posteriori Inference with Entropic Priors

In certain applications it is useful to fit multinomial distributions to observed data with a penalty term that encourages sparsity. For example, in probabilistic latent audio source decomposition one may wish to encode the assumption that only a few latent sources are active at any given time. The standard heuristic of applying an L1 penalty is not an option when fitting the parameters to a mu...

Local Maximum Likelihood Estimation and Inference