Results 51 to 60 of about 2,810,332 (372)

Revisiting maximum-a-posteriori estimation in log-concave models [PDF]

, 2019
Maximum-a-posteriori (MAP) estimation is the main Bayesian estimation methodology in imaging sciences, where high dimensionality is often addressed by using Bayesian models that are log-concave and whose posterior mode can be computed efficiently by ...
Pereyra, Marcelo
core   +2 more sources

Trusted K Nearest Bayesian Estimation for Indoor Positioning System

IEEE Access, 2019
Indoor positioning systems have received increasing attention because of their wide range of indoor applications. However, the positioning system generally suffers from a large error in localization and has low solidity.
Rohan Kumar Yadav, Bimal Bhattarai, Hui-Seon Gang, Jae-Young Pyun   +3 more
semanticscholar   +1 more source

Bayesian estimation for quantum sensing in the absence of single-shot detection [PDF]

Physical review B, 2018
Quantum information protocols, such as quantum error correction and quantum phase estimation, have been widely used to enhance the performance of quantum sensors.
H. T. Dinani, D. Berry, Raúl B. Gonzalez, J. Maze, C. Bonato   +4 more
semanticscholar   +1 more source

Machine-Learning-Based Parameter Estimation of Gaussian Quantum States

IEEE Transactions on Quantum Engineering, 2022
In this article, we propose a machine-learning framework for parameter estimation of single-mode Gaussian quantum states. Under a Bayesian framework, our approach estimates parameters of suitable prior distributions from measured data.
Neel Kanth Kundu, Matthew R. McKay, Ranjan K. Mallik   +2 more
doaj   +1 more source

A Parameterization Invariant Approach to the Statistical Estimation of the CKM Phase $\alpha$ [PDF]

, 2008
In contrast to previous analyses, we demonstrate a Bayesian approach to the estimation of the CKM phase $\alpha$ that is invariant to parameterization.
C.P. Robert, D.S. Sivia with J. Skilling, F.J. Botella, H.B. Prosper, J. Charles, J. Charles, Johann Cohen-Tanugi, Particle Data Group collaboration, Robin D Morris, UTfit collaboration   +9 more
core   +5 more sources

Frequentist and Bayesian Quantum Phase Estimation

, 2018
Frequentist and Bayesian phase estimation strategies lead to conceptually different results on the state of knowledge about the true value of the phase shift.
Augusto Smerzi, Luca Pezzè, Manuel Gessner, Weidong Li, Yan Li, Zhihong Ren   +5 more
core   +2 more sources

Motion estimation with object based regularisation [PDF]

, 1996
A dynamic programming based matching method for motion estimation, that optimises a Bayesian maximum likelihood function in a 3-D optimisation space, is presented.
Cosmas, J, Panis, S
core   +1 more source

Bayesian Sperm Competition Estimates

Genetics, 2003
Abstract We introduce a Bayesian method for estimating parameters for a model of multiple mating and sperm displacement from genotype counts of brood-structured data. The model is initially targeted for Drosophila melanogaster, but is easily adapted to other organisms. The method is appropriate for use with field studies where the number
Beatrix, Jones, Andrew G, Clark
openaire   +2 more sources

Distance Estimation in Cosmology

, 1995
In this paper we outline the framework of mathematical statistics with which one may study the properties of galaxy distance estimators. We describe, within this framework, how one may formulate the problem of distance estimation as a Bayesian inference ...
Bertschinger, Chamcham, Dekel, Dekel, Dekel, Dressler, Dressler, Eastman, Eddington, Faber, Feast, Federspiel, Fiegelson, Fisher, Freedman, Freudling, Giraud, Gould, Hamuy, Heavens, Hendry, Hendry, Hendry, Hendry, Hendry, Hoel, Hogg, Hudson, Hudson, J.F.L. Simmons, Jacoby, Kanbur, Kendall, Kirschner, Lahav, Landy, Lauer, Lynden-Bell, M.A. Hendry, Malmquist, Malmquist, Mathewson, Mood, Mould, Newsam, Newsam, Nusser, Pierce, Pierce, Rauzy, Riess, Riess, Rubin, Saha, Saha, Salucci, Sandage, Sandage, Sandage, Sandage, Schechter, Schmidt, Schmidt, Shaya, Shaya, Simmons, Sodré, Teerikorpi, Tonry, Triay, Tully, van den Bergh, Vaughan, Visvanathan, von Mises, Willick, Zaccheo   +76 more
core   +1 more source

Variational semi-blind sparse deconvolution with orthogonal kernel bases and its application to MRFM [PDF]

, 2013
We present a variational Bayesian method of joint image reconstruction and point spread function (PSF) estimation when the PSF of the imaging device is only partially known.
Almeida, Babacan, Besag, Bishop, Chao, Chenegros, Daubechies, Degen, Dobigeon, Figueiredo, Forbes, Forbes, Ge, Gilks, Kundur, Landweber, Makni, Makni, Mittelman, Nasios, Orieux, Park, Pillonetto, Robert, Rugar, Shan, Sidles, Sidles, Sidles, Themelis, Ting, Tzikas, Vincent, Walker, Wang, Ward, Winn, Züger, Züger, Züger   +39 more
core   +5 more sources