The Ornstein-Uhlenbeck Dirichlet process and other time-varying processes for Bayesian nonparametric inference
DOI10.1016/j.jspi.2011.05.019zbMath1219.62084OpenAlexW1963946426MaRDI QIDQ2276197
Publication date: 1 August 2011
Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.jspi.2011.05.019
Ornstein-Uhlenbeck processDirichlet processparticle filteringnormalised random measures with independent incrementstime-dependent Bayesian nonparametrics
Computational methods in Markov chains (60J22) Applications of statistics to actuarial sciences and financial mathematics (62P05) Bayesian inference (62F15) Inference from stochastic processes (62M99) Random measures (60G57) Nonparametric inference (62G99)
Related Items (5)
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Inference with non-Gaussian Ornstein-Uhlenbeck processes for stochastic volatility
- Stick-breaking autoregressive processes
- Stochastic volatility modelling in continuous time with general marginal distributions: inference, prediction and model selection
- Fractional Ornstein-Uhlenbeck Lévy processes and the telecom process: Upstairs and downstairs
- Ferguson distributions via Polya urn schemes
- Slice sampling. (With discussions and rejoinder)
- Distributional results for means of normalized random measures with independent increments
- Nonparametric Bayesian data analysis
- Normalized random measures driven by increasing additive processes
- Bayesian inference with stochastic volatility models using continuous superpositions of non-Gaussian Ornstein-Uhlenbeck processes
- A Bayesian analysis of some nonparametric problems
- Non-Gaussian Ornstein–Uhlenbeck-based Models and Some of Their Uses in Financial Economics
- Sequential Monte Carlo Methods in Practice
- Superposition of Ornstein--Uhlenbeck Type Processes
- Posterior Analysis for Normalized Random Measures with Independent Increments
- Gibbs Sampling for Bayesian Non-Conjugate and Hierarchical Models by Using Auxiliary Variables
- Sequential importance sampling for nonparametric Bayes models: The next generation
- Bayesian Inference for Linear Dynamic Models With Dirichlet Process Mixtures
- Bayesian Inference for Non-Gaussian Ornstein–Uhlenbeck Stochastic Volatility Processes
- Gibbs Sampling Methods for Stick-Breaking Priors
- Controlling the Reinforcement in Bayesian Non-Parametric Mixture Models
- Bayesian Semiparametric Dynamic Frailty Models for Multiple Event Time Data
- A Representation of Independent Increment Processes without Gaussian Components
- Analysis of Financial Time Series
- Hierarchical Mixture Modeling With Normalized Inverse-Gaussian Priors
- Order-Based Dependent Dirichlet Processes
This page was built for publication: The Ornstein-Uhlenbeck Dirichlet process and other time-varying processes for Bayesian nonparametric inference