Sequential Decentralized Parameter Estimation Under Randomly Observed Fisher Information
نویسندگان
چکیده
منابع مشابه
Parameter estimation for partially observed queues
Absbacr-In this paper, we consider parameter estimation for a FIFO queue with deterministic service times and two independent arrival streams of “observed” and “unobserved” packets. The arrivals of unobserved packets are Poisson with an unknown rate X while the arrivals of observed packets are arbitrary. Maximum likelihood estimation of X is formulated based on the arrival times and waiting tim...
متن کامل4 : Parameter Estimation in Fully Observed BNs
The goal of learning a graphical model is find the best (or most likely) Bayesian Network (both DAG and CPDs) given a set of independent samples (or assignments of random variables). To succeed in finding the best model first, we either learn the structure of the model or assume a structure of the model given by an expert. Structural leaning has multiple limitations and hence there is not too m...
متن کاملAnalysis of Fisher Information and the Cramér-Rao Bound for Nonlinear Parameter Estimation After Random Compression
In this paper, we analyze the impact of compression with complex random matrices on Fisher information and the Cramér-Rao Bound (CRB) for estimating unknown parameters in the mean value function of a complex multivariate normal distribution. We consider the class of random compression matrices whose distribution is right-unitarily invariant. The compression matrix whose elements are i.i.d. stan...
متن کاملFisher information under decoherence in Bloch representation
Wei Zhong,1,2 Zhe Sun,1,3 Jian Ma,1,2 Xiaoguang Wang,1,2,* and Franco Nori1,4 1Advanced Science Institute, RIKEN, Wako-shi, Saitama 351-0198, Japan 2Zhejiang Institute of Modern Physics, Department of Physics, Zhejiang University, Hangzhou 310027, China 3Department of Physics, Hangzhou Normal University, Hangzhou 310036, China 4Physics Department, The University of Michigan, Ann Arbor, Michigan...
متن کاملAdaptive Compressive Imaging via Sequential Parameter Estimation
We describe a compressive imager that adapts the measurement basis based on past measurements within a sequential Bayesian estimation framework. Simulation study shows a 7% improvement in reconstruction performance compared to a static measurement basis. © 2011 Optical Society of America OCIS codes: 110.1758,110.1085,100.3190.
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 Information Theory
سال: 2014
ISSN: 0018-9448,1557-9654
DOI: 10.1109/tit.2013.2292062