Approximate inference in state space models with intractable likelihoods using Gaussian process optimisation
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Summary
A novel method for MAP parameter inference in nonlinear state space models with intractable likelihoods is proposed based on a combination of Gaussian process optimisation, sequentially Optimised Process Optimisation and Sequential Optimisation.
- Type
- article
- Published
- 2014-01-01
- Cited by
- 5
- References
- 28
- Access
- Open access
- OpenAlex
- https://openalex.org/W204266964
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:18163284
Keywords
Inference, Gaussian process, State space, Gaussian, Algorithm
References
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- Optimization-Theory And Applications
- Bayesian Inference for Generalised Markov Switching Stochastic Volatility Models
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- Particle Filter-Based Gaussian Process Optimisation for Parameter Inference
- Simple consistent estimators of stable distribution parameters
- Adaptive approximate Bayesian computation
- The Behavior of Stock-Market Prices
- Novel approach to nonlinear/non-Gaussian Bayesian state estimation
- A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning
- Lipschitzian optimization without the Lipschitz constant
- Markov chain Monte Carlo methods for stochastic volatility models
- Filtering via approximate Bayesian computation
- Practical Bayesian Optimization of Machine Learning Algorithms
- Approximate Bayesian computational methods
- Parameter Estimation in Hidden Markov Models With Intractable Likelihoods Using Sequential Monte Carlo
- Bayesian analysis of stochastic volatility models with fat-tails and correlated errors
- Scalable inference for Markov processes with intractable likelihoods
Cited by
- Sequential Monte Carlo for inference in nonlinear state space models
- Quasi-Newton particle Metropolis-Hastings applied to intractable likelihood models
- Accelerating Monte Carlo methods for Bayesian inference in dynamical models
- Quasi-Newton particle Metropolis-Hastings
- Multi-fidelity cost-aware Bayesian optimization
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