381 research outputs found
Bayesian Change Point Analysis of Copy Number Variants Using Human Next Generation Sequencing Data
Title from PDF of title page, viewed on June 8, 2015Dissertation advisor: Jie ChenVitaIncludes bibliographic references (pages 127-134)Thesis (Ph.D.)--Department of Mathematics and Statistics and School of Biological Sciences. University of Missouri--Kansas City, 2014Read count analysis is the principal strategy implemented in detection of copy number variants using human next generation sequencing (NGS) data. Read count data
from NGS has been demonstrated to follow non homogeneous Poisson distributions.
The current change point analysis methods for detection of copy number variants are
based on normal distribution assumption and used ordinary normal approximation in
their algorithms. To improve sensitivity and reduce false positive rate for detection
of copy number variants, we developed three models: one Bayesian Anscombe normal approximation model for single genome, one Bayesian Poisson model for single
genome, and a Bayesian Anscome normal approximation model for paired genome.
The Bayesian statistics have been optimized for detection of change points and copy
numbers at single and multiple change points through Monte Carlo simulations. Three
R packages based on these models have been built up to simulate Poisson distribution data, estimate and display copy number variants in table and graphics. The high
sensitivity and specificity of these models have been demonstrated in simulated read
count data with known Poisson distribution and in human NGS read count data as
well in comparison to other popular packages.Background -- Single genome Bayesian approaches in NGS read count analysis -- Normal approximation Batesian change point model for paired genomes -- Conclusion and future wor
Precise Request Tracing and Performance Debugging for Multi-tier Services of Black Boxes
As more and more multi-tier services are developed from commercial components
or heterogeneous middleware without the source code available, both developers
and administrators need a precise request tracing tool to help understand and
debug performance problems of large concurrent services of black boxes.
Previous work fails to resolve this issue in several ways: they either accept
the imprecision of probabilistic correlation methods, or rely on knowledge of
protocols to isolate requests in pursuit of tracing accuracy. This paper
introduces a tool named PreciseTracer to help debug performance problems of
multi-tier services of black boxes. Our contributions are two-fold: first, we
propose a precise request tracing algorithm for multi-tier services of black
boxes, which only uses application-independent knowledge; secondly, we present
a component activity graph abstraction to represent causal paths of requests
and facilitate end-to-end performance debugging. The low overhead and tolerance
of noise make PreciseTracer a promising tracing tool for using on production
systems
Multiple Descent in the Multiple Random Feature Model
Recent works have demonstrated a double descent phenomenon in
over-parameterized learning. Although this phenomenon has been investigated by
recent works, it has not been fully understood in theory. In this paper, we
investigate the multiple descent phenomenon in a class of multi-component
prediction models. We first consider a ''double random feature model'' (DRFM)
concatenating two types of random features, and study the excess risk achieved
by the DRFM in ridge regression. We calculate the precise limit of the excess
risk under the high dimensional framework where the training sample size, the
dimension of data, and the dimension of random features tend to infinity
proportionally. Based on the calculation, we further theoretically demonstrate
that the risk curves of DRFMs can exhibit triple descent. We then provide a
thorough experimental study to verify our theory. At last, we extend our study
to the ''multiple random feature model'' (MRFM), and show that MRFMs ensembling
types of random features may exhibit -fold descent. Our analysis
points out that risk curves with a specific number of descent generally exist
in learning multi-component prediction models.Comment: 89 pages, 9 figures. Version 3 adds new description of triple descent
in certain double random feature model, deletes the discussion of NTK
regimes, and adds more literature reference
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