Bayesian Segmentation in Signal with Multiplicative Noise Using Reversible Jump MCMC
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Summary
The Reversible Jump Markov chain Monte Carlo (MCMC) method is adopted to overcome the problem of signal segmentation where the signal is disturbed by multiplicative noise where the number of segments is unknown.
- Type
- article
- Published
- 2018-04-01
- Cited by
- 5
- References
- 12
- Access
- Open access
- OpenAlex
- https://openalex.org/W2782571422
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:67305493
Keywords
Markov chain Monte Carlo, Reversible-jump Markov chain Monte Carlo, Bayesian probability, Algorithm, Posterior probability
References
- The Bayesian choice : from decision-theoretic foundations to computational implementation
- Understanding Synthetic Aperture Radar Images
- Hierarchical Bayesian of ARMA Models Using Simulated Annealing Algorithm
- Changepoint detection using reversible jump MCMC methods
- Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
- Hierarchical Bayesian segmentation of signals corrupted by multiplicative noise
- Adaptive detection of known signals in additive noise by means of kernel density estimators
- The Noisy Expectation-Maximization Algorithm for Multiplicative Noise Injection
- A new variational approach for restoring images with multiplicative noise
- An Adaptive Fractional-Order Variation Method for Multiplicative Noise Removal
- Sparse analysis model based multiplicative noise removal with enhanced regularization
- Monte Carlo Statistical Methods
Cited by
- Hierarchical Bayesian segmentation for piecewise stationary autoregressive model based on reversible jump MCMC
- Bayesian Estimation in Piecewise Constant Model with Gamma Noise by Using Reversible Jump MCMC
- Risk assessment optimization for decision support using intelligent model based on fuzzy inference renewable rules
- Evaluation of phase-frequency instability when processing complex radar signals
- Adaptive segmentation algorithm based on level set model in medical imaging
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