Probabilistic Detection and Estimation of Conic Sections From Noisy Data

DOI10.1080/10618600.2020.1737084OpenAlexW3009833174MaRDI QIDQ5065999

Subharup Guha, Sujit Kumar Ghosh

Publication date: 28 March 2022

Published in: Journal of Computational and Graphical Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1910.14078

zbMATH Keywords

Markov chain Monte CarloMetropolis-Hastings algorithmBayesian hierarchical modelBernstein basis polynomialsfocus-directrix approachpartial conics

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (1)

BayesConics

Uses Software

• BayesDA

Cites Work

• Goodness-of-fit tests and model validity. Papers from the international conference, Paris, France, May 29--31, 2000
• Consistent estimation in an implicit quadratic measurement error model
• Weak convergence and optimal scaling of random walk Metropolis algorithms
• Application of Bernstein polynomials for smooth estimation of a distribution and density function
• Unimodal density estimation using Bernstein polynomials
• Convergence rates for density estimation with Bernstein polynomials.
• ElliFit: an unconstrained, non-iterative, least squares based geometric ellipse fitting method
• Geometry of Conics
• Fundamentals of Nonparametric Bayesian Inference
• Least-squares orthogonal distances fitting of circle, sphere, ellipse, hyperbola, and parabola
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item

This page was built for publication: Probabilistic Detection and Estimation of Conic Sections From Noisy Data