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An efficient GPU-parallel coordinate descent algorithm for sparse precision matrix estimation via scaled lasso
The sparse precision matrix plays an essential role in the Gaussian graphical model since a zero off-diagonal element indicates conditional...
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Massively Parallel Path Space Filtering
Restricting path tracing to a small number of paths per pixel in order to render images faster rarely achieves a satisfactory image quality for...
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An efficient parallel block coordinate descent algorithm for large-scale precision matrix estimation using graphics processing units
Large-scale sparse precision matrix estimation has attracted wide interest from the statistics community. The convex partial correlation selection...
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Software for Numerical Linear Algebra
There is a variety of computer software available to perform the operations on vectors and matrices discussed in Chap. 11 and previous chapters. I...
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Scalable Bayesian inference for self-excitatory stochastic processes applied to big American gunfire data
The Hawkes process and its extensions effectively model self-excitatory phenomena including earthquakes, viral pandemics, financial transactions,...
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A simple method for rejection sampling efficiency improvement on SIMT architectures
We derive a probability distribution for the possible number of iterations required for a SIMT (single instruction multiple thread) program using...
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Application of the sequential matrix diagonalization algorithm to high-dimensional functional MRI data
This paper introduces an adaptation of the sequential matrix diagonalization (SMD) method to high-dimensional functional magnetic resonance imaging...
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Predicting the Content of the Main Components of Gardeniae Fructus Praeparatus Based on Deep Learning
Gardeniae Fructus (GF) and its stir-fried product, Gardeniae Fructus Praeparatus (GFP), are commonly used herbal medicines in traditional Chinese...
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Recursive Modified Pattern Search on High-Dimensional Simplex : A Blackbox Optimization Technique
In this paper, a novel derivative-free pattern search based algorithm for Black-box optimization is proposed over a simplex constrained parameter...
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Hamiltonian Markov chain Monte Carlo for partitioned sample spaces with application to Bayesian deep neural nets
Allocating computation over multiple chains to reduce sampling time in MCMC is crucial in making MCMC more applicable in the state of the art models...
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Anomaly Detection in Financial Transactions Via Graph-Based Feature Aggregations
Anomaly detection in the financial domain aims to detect abnormal transactions such as fraudulent transactions that can lead to loss of revenues to...
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OpBerg: Discovering Causal Sentences Using Optimal Alignments
The biological literature is rich with sentences that describe causal relations. Methods that automatically extract such sentences can help...
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Distributed quantile regression for longitudinal big data
Longitudinal data, measurements taken from the same subjects over time, appear routinely in many scientific fields, such as biomedical science,...
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On the efficient implementation of classification rule learning
Rule learning methods have a long history of active research in the machine learning community. They are not only a common choice in applications...
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Modern Machine Learning Methods for Time Series Analysis
Artificial intelligence (AI) has gained considerable achievements over the last decades, and AI methods have been proposed as alternatives to...
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Estimation of the Basic LiNGAM Model
This chapter discusses estimation methods for the coefficient matrix...
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Fundamentals of Auditing Financial Reports
Audits are independent examinations of the records of an organization to ascertain how far the financial statements present a true picture of the...
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Monte Carlo Methods for the Propagation of Uncertainties
This chapter introduces Monte Carlo methods for calculating uncertainty in indirect measurements. In the Monte Carlo method, each input variable to...
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A Case Study Competition Among Methods for Analyzing Large Spatial Data
The Gaussian process is an indispensable tool for spatial data analysts. The onset of the “big data” era, however, has lead to the traditional...
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