Name
Abstract | ||
|---|---|---|
We propose a Bayesian conjugate framework for inferring multiple phases. The framework requires a generalisation of the von Mises distribution for multiple variables. The principal difficulty in the generalisation is the computation of the first order moment and the normalising constant which are essential for Bayesian inference. We propose two approaches, one based on a Bessel function expansion and the other based on a Markov Chain Monte Carlo technique using the Gibbs sampler. We then assess the performance of these two methods against variations in parameters of the generalised von Mises distribution. |
Year | Venue | Keywords |
|---|---|---|
2012 | Information Fusion | Bayes methods,Bessel functions,Markov processes,Monte Carlo methods,phase estimation,signal sampling,Bayesian conjugate analysis,Bayesian inference,Bessel function expansion,Gibbs sampler,Markov chain Monte Carlo technique,first order moment,multiple phase estimation,normalising constant,von Mises distribution,Bayesian analysis,Gibbs,MCMC,conjugate prior,multiple phase estimation,multivariate circular regression,von Mises distribution |
Field | DocType | ISBN |
Applied mathematics,Bayesian inference,Markov chain Monte Carlo,Computer science,Bayesian linear regression,Artificial intelligence,Gibbs sampling,Mathematical optimization,Monte Carlo method,von Mises distribution,Conjugate prior,Machine learning,Bayesian probability | Conference | 978-0-9824438-4-2 |
Citations | PageRank | References |
1 | 0.39 | 3 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bentarage Sachintha Karunaratne | 1 | 4 | 1.30 |
| Mark R. Morelande | 2 | 118 | 15.73 |
| B. Moran | 3 | 111 | 21.09 |