Maximum a Posteriori Estimation for Information Source Detection
نویسندگان
چکیده
منابع مشابه
Maximum a posteriori multiple source localization with Gibbs Sampling
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...
متن کاملPopulation pharmacokinetic/pharmacodynamic mixture models via maximum a posteriori estimation
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...
متن کاملMaximum a Posteriori Estimation of Coupled Hidden Markov Models
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 ...
متن کاملBatch Maximum Likelihood (ML) and Maximum A Posteriori (MAP) Estimation With Process Noise for Tracking Applications
Batch maximum likelihood (ML) and maximum a posteriori (MAP) estimation with process noise is now more than thirty-five years old, and its use in multiple target tracking has long been considered to be too computationally intensive for real-time applications. While this may still be true for general usage, it is ideally suited for special needs such as bias estimation, track initiation and spaw...
متن کاملMaximum a posteriori sequence estimation using Monte Carlo particle filters
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...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Systems, Man, and Cybernetics: Systems
سال: 2020
ISSN: 2168-2216,2168-2232
DOI: 10.1109/tsmc.2018.2811410