Perspective maximum likelihood-type estimation via proximal decomposition

Kullback proximal algorithims for maximum-likelihood estimation

Kullback Proximal Algorithms for Maximum Likelihood Estimation

Accelerated algorithms for maximum likelihood image reconstruction are essential for emerging applications such as 3D tomography, dynamic tomographic imaging, and other high dimensional inverse problems. In this paper, we introduce and analyze a class of fast and stable sequential optimization methods for computing maximum likelihood estimates and study its convergence properties. These methods...

Maximum-Likelihood Circle-Parameter Estimation via Convolution

In this paper, we present an interpretation of the Maximum Likelihood Estimator (MLE) and the Delogne-Kåsa Estimator (DKE) for circle-parameter estimation via convolution. Under a certain model for theoretical images, this convolution is an exact description of the MLE. We use our convolution based MLE approach to find good starting estimates for the parameters of a circle, that is, the centre ...

Parameter Estimation via Gaussian Processes and Maximum Likelihood Estimation

Computer models usually have a variety of parameters that can (and need to) be tuned so that the model better reflects reality. This problem is called calibration and is an inverse problem. We assume that we have a set of observed responses to given inputs in a physical system and a computer model that depends on parameters that models the physical system being studied. It is often the case tha...

Maximum Likelihood Estimation of Parameters in Generalized Functional Linear Model

Sometimes, in practice, data are a function of another variable, which is called functional data. If the scalar response variable is categorical or discrete, and the covariates are functional, then a generalized functional linear model is used to analyze this type of data. In this paper, a truncated generalized functional linear model is studied and a maximum likelihood approach is used to esti...