Technical Note: Determining Peeling Order Using Sparse Matrix Algorithms

Technical note: determining peeling order using sparse matrix algorithms.

To study the effect of individual genes by segregation or linkage analyses, the likelihood of the model needs to be evaluated. The likelihood can be computed efficiently using the Elston-Stewart algorithm. This algorithm involves summing over the unobserved genotypes in the pedigree, which is called peeling. An important aspect of this algorithm is to determine the order of peeling to maximize ...

GDAGsim: Sparse matrix algorithms for Bayesian computation

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.

Sparse Matrix Reordering Algorithms for Cluster Identification

Comparative Analysis of Sparse Matrix Algorithms

We evaluate and compare the storage efficiency of different sparse matrix storage formats as index structure for text collection and their corresponding sparse matrixvector multiplication algorithm to perform query processing in information retrieval (IR) application. We show the results of our implementations for several sparse matrix algorithms such as Coordinate Storage (COO), Compressed Spa...

Combinatorial Algorithms for Parallel Sparse Matrix Distributions

Combinatorial algorithms have long played a crucial enabling role in parallel computing. An important problem in sparse matrix computations is how to distribute the sparse matrix and vectors among processors. We review graph, bipartite graph, and hypergraph models for both 1d (row or column) distributions and 2d distributions. A valuable tool is hypergraph partitioning. We present results using...