Homodyned K-distribution: parameter estimation and uncertainty quantification using Bayesian neural networks.
Ali Kafaei Zad TehraniIván M. Rosado-MendezHassan RivazPublished in: CoRR (2022)
Keyphrases
- parameter estimation
- maximum likelihood
- neural network
- posterior distribution
- bayesian model selection
- least squares
- em algorithm
- model selection
- markov random field
- markov chain monte carlo
- parameter estimation algorithm
- gaussian distribution
- statistical models
- expectation maximization
- parameter values
- maximum likelihood estimation
- approximate inference
- metropolis hastings algorithm
- posterior probability
- maximum likelihood estimates
- random fields
- exponential family
- bayesian networks
- model fitting
- dempster shafer
- gibbs sampling
- probability distribution
- likelihood function
- parameter estimates
- estimation problems
- probability density function
- position estimation
- parameters estimation
- machine learning
- image sequences
- feature selection
- particle physics
- pairwise
- belief functions
- random variables
- conditional probabilities
- dynamic model