Marginal likelihoods for non-Gaussian models using auxiliary mixture sampling

DOI10.1016/j.csda.2008.03.028zbMath1452.62060OpenAlexW2079965086MaRDI QIDQ1023812

Helga Wagner, Sylvia Frühwirth-Schnatter

Publication date: 16 June 2009

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2008.03.028

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Bayesian inference (62F15)

Related Items (11)

Modelling breaks and clusters in the steady states of macroeconomic variables ⋮ Stochastic model specification search for Gaussian and partial non-Gaussian state space models ⋮ Marginal Likelihood Estimation with the Cross-Entropy Method ⋮ A comparative study of Monte Carlo methods for efficient evaluation of marginal likelihood ⋮ On marginal likelihood computation in change-point models ⋮ From here to infinity: sparse finite versus Dirichlet process mixtures in model-based clustering ⋮ Estimating marginal likelihoods from the posterior draws through a geometric identity ⋮ Pitfalls of estimating the marginal likelihood using the modified harmonic mean ⋮ A class of adaptive importance sampling weighted EM algorithms for efficient and robust posterior and predictive simulation ⋮ The effect of rounding on payment efficiency ⋮ Specification tests for time-varying parameter models with stochastic volatility

Uses Software

• tsbridge

Cites Work

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• Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
• Stochastic volatility with leverage: fast and efficient likelihood inference
• Auxiliary mixture sampling with applications to logistic models
• Bayesian analysis of dichotomous quantal response models
• Estimating the dimension of a model
• Markov chain Monte Carlo methods for stochastic volatility models.
• Finite mixture and Markov switching models.
• Auxiliary mixture sampling for parameter-driven models of time series of counts with applications to state space modelling
• Marginal Likelihood from the Gibbs Output
• Estimating marginal likelihoods for mixture and Markov switching models using bridge sampling techniques*
• Dynamic Generalized Linear Models and Bayesian Forecasting
• Outliers in Circular Data
• DATA AUGMENTATION AND DYNAMIC LINEAR MODELS
• On Gibbs sampling for state space models
• Computing Bayes Factors by Combining Simulation and Asymptotic Approximations
• Filtering via Simulation: Auxiliary Particle Filters
• Approximate Inference in Generalized Linear Mixed Models
• Bayesian Inference in Econometric Models Using Monte Carlo Integration
• Marginal Likelihood From the Metropolis–Hastings Output
• The simulation smoother for time series models
• Time Series Analysis of Non-Gaussian Observations Based on State Space Models from Both Classical and Bayesian Perspectives
• Methods and Criteria for Model Selection
• Linear mixed models for longitudinal data
• A new look at the statistical model identification

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