Approximating Bayes in the 21st century
The 21st century has seen an enormous growth in the development and use of approximate
Bayesian methods. Such methods produce computational solutions to certain “intractable” …
Bayesian methods. Such methods produce computational solutions to certain “intractable” …
Computing Bayes: Bayesian computation from 1763 to the 21st century
The Bayesian statistical paradigm uses the language of probability to express uncertainty
about the phenomena that generate observed data. Probability distributions thus …
about the phenomena that generate observed data. Probability distributions thus …
[图书][B] Hamiltonian Monte Carlo methods in machine learning
Hamiltonian Monte Carlo Methods in Machine Learning introduces methods for optimal
tuning of HMC parameters, along with an introduction of Shadow and Non-canonical HMC …
tuning of HMC parameters, along with an introduction of Shadow and Non-canonical HMC …
The Bayesian validation metric: a framework for probabilistic model calibration and validation
T Tohme - 2020 - dspace.mit.edu
In model development, model calibration and validation play complementary roles toward
learning reliable models. In this thesis, we propose and develop the" Bayesian Validation …
learning reliable models. In this thesis, we propose and develop the" Bayesian Validation …
[图书][B] Hybrid Monte Carlo methods in machine learning: stochastic volatility methods, shadow Hamiltonians, adaptive approaches and variance reduction techniques
WT Mongwe - 2022 - search.proquest.com
Abstract Markov Chain Monte Carlo (MCMC) methods are a vital inference tool for
probabilistic machine learning models. A commonly utilised MCMC algorithm is the …
probabilistic machine learning models. A commonly utilised MCMC algorithm is the …