GDAGsim: Sparse matrix algorithms for Bayesian computation
نویسنده
چکیده
GDAGsim is a software library which can be used to carry out conditional sampling of linear Gaussian directed acyclic graph models, and hence can be used for the implementation of efficient block MCMC samplers for such models. This paper examines the software library and its design, and how it can be applied to problems in Bayesian inference.
منابع مشابه
A sparse matrix approach to Bayesian computation in large linear models
This paper examines the problem of efficient Bayesian computation in the context of linear Gaussian Directed Acyclic Graph (DAG) models. Unobserved latent variables are grouped together in a block, and sparse matrix techniques for computation are explored. Conditional sampling and likelihood computations are shown to be straightforward using a sparse matrix approach, allowing MCMC algorithms wi...
متن کاملISAR Imaging Based on L1 L0 Norms Homotopy 2D Block Sparse Signal Recovery Algorithm
Many traditional sparse signal recovery based ISAR imaging methods did not utilize the block scatterers information of targets. Some block Bayesian learning based ISAR imaging algorithms are computational expensive. In this paper, a 2D block 1 0 norms homotopy sparse signal recovery algorithm (the BL1L0 algorithm) is proposed and utilized to form the ISAR image. Compared with Bayesian-based alg...
متن کاملAn Adaptive Data Collection Algorithm Based on a Bayesian Compressed Sensing Framework
For Wireless Sensor Networks, energy efficiency is always a key consideration in system design. Compressed sensing is a new theory which has promising prospects in WSNs. However, how to construct a sparse projection matrix is a problem. In this paper, based on a Bayesian compressed sensing framework, a new adaptive algorithm which can integrate routing and data collection is proposed. By introd...
متن کاملFast Reconstruction of SAR Images with Phase Error Using Sparse Representation
In the past years, a number of algorithms have been introduced for synthesis aperture radar (SAR) imaging. However, they all suffer from the same problem: The data size to process is considerably large. In recent years, compressive sensing and sparse representation of the signal in SAR has gained a significant research interest. This method offers the advantage of reducing the sampling rate, bu...
متن کاملA geometric framework for sparse matrix problems
In this paper, we set up a geometric framework for solving sparse matrix problems. We introduce geometric sparseness, a notion which applies to several well-known families of sparse matrix. Two algorithms are presented for solving geometrically-sparse matrix problems. These algorithms are inspired by techniques in classical algebraic topology, and involve the construction of a simplicial comple...
متن کامل