A new class of stochastic EM algorithms. Escaping local maxima and handling intractable sampling
From MaRDI portal
Publication:830097
DOI10.1016/j.csda.2020.107159OpenAlexW3118823227MaRDI QIDQ830097
Juliette Chevallier, Stéphanie Allassonnière
Publication date: 7 May 2021
Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.csda.2020.107159
stochastic optimizationstochastic approximationtempered distributiontheoretical convergenceEM-like algorithm
Related Items (4)
Global implicit function theorems and the online expectation–maximisation algorithm ⋮ Randomized extrapolation for accelerating EM-type fixed-point algorithms ⋮ Missing link survival analysis with applications to available pandemic data ⋮ On the curved exponential family in the Stochastic Approximation Expectation Maximization Algorithm
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Optimization by Simulated Annealing
- Damped Anderson Acceleration With Restarts and Monotonicity Control for Accelerating EM and EM-like Algorithms
- Measuring the component overlapping in the Gaussian mixture model
- Statistical guarantees for the EM algorithm: from population to sample-based analysis
- Construction of Bayesian deformable models via a stochastic approximation algorithm: a convergence study
- Markov chains and stochastic stability
- A quasi-Newton acceleration for high-dimensional optimization algorithms
- Approximate Bayesian computational methods
- Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models
- Quasi-Monte Carlo sampling to improve the efficiency of Monte Carlo EM
- Maximum likelihood estimation in nonlinear mixed effects models
- Finite mixture models and model-based clustering
- Extension of the SAEM algorithm to left-censored data in nonlinear mixed-effects model: Application to HIV dynamics model
- On the convergence properties of the EM algorithm
- The geometry of exponential families
- Independent component analysis, a new concept?
- Convergence of the Monte Carlo expectation maximization for curved exponential families.
- Coupling stochastic EM and approximate Bayesian computation for parameter inference in state-space models
- Convergence of a stochastic approximation version of the EM algorithm
- Rates of convergence of the Hastings and Metropolis algorithms
- An introduction to variational methods for graphical models
- f-SAEM: a fast stochastic approximation of the EM algorithm for nonlinear mixed effects models
- Inference in hidden Markov models.
- A stochastic algorithm for probabilistic independent component analysis
- Graphical Models, Exponential Families, and Variational Inference
- Simple and Globally Convergent Methods for Accelerating the Convergence of Any EM Algorithm
- Acceleration of Stochastic Approximation by Averaging
- Maximizing Generalized Linear Mixed Model Likelihoods With an Automated Monte Carlo EM Algorithm
- Asymptotic Statistics
- Efficient Gaussian Sampling for Solving Large-Scale Inverse Problems Using MCMC
- Coupling a stochastic approximation version of EM with an MCMC procedure
- Stability of Stochastic Approximation under Verifiable Conditions
- Coupling and Ergodicity of Adaptive Markov Chain Monte Carlo Algorithms
- A Stochastic Approximation Method
This page was built for publication: A new class of stochastic EM algorithms. Escaping local maxima and handling intractable sampling
