Multiple source localization in underwater environments is approached within a matchedfield processing framework. A Maximum a Posteriori Estimation method is proposed that estimates source location and spectral characteristics of multiple sources via Gibbs Sampling. The method facilitates localization of weak sources which are typically masked by the presence of strong interferers. A performanc...

Maximum a posteriori estimation (MAP) Point estimates Manually specified Manually specified Modest Classical Bayesian estimation Posterior distributions Manually specified Manually specified Good (1) Nonparametric Bayesian estimation Posterior distributions Posterior distributions ―――― Excellent (2) Hierarchical Posterior distributions ―――― Posterior distributions Excellent Parameters Complexit...

Pharmacokinetic/pharmacodynamic phenotypes are identified using nonlinear random effects models with finite mixture structures. A maximum a posteriori probability estimation approach is presented using an EM algorithm with importance sampling. Parameters for the conjugate prior densities can be based on prior studies or set to represent vague knowledge about the model parameters. A detailed sim...

Coupled Hidden Markov Models (CHMM) are a tool which model interactions between variables in state space rather than observation space. Thus they may reveal coupling in cases where classical tools such as correlation fail. In this paper we derive the maximum a posteriori equations for the Expectation Maximization algorithm. The use of the models is demonstrated on simulated data, as well as in ...

We develop methods for performing maximum a posteriori (MAP) sequence estimation in non-linear non-Gaussian dynamic models. The methods rely on a particle cloud representation of the filtering distribution which evolves through time using importance sampling and resampling ideas. MAP sequence estimation is then performed using a classical dynamic programming technique applied to the discretised...