Jeffrey D. Scargle

发表

Instantaneous Spectrum Estimation from Event-Based Densities

Leon Cohen, Lorenzo Galleani, Douglas J. Nelson, 2002, EURASIP J. Adv. Signal Process..

Optimal Partitions of Data In Higher Dimensions

Jeffrey D. Scargle, Bradley W. Jackson, David Barnes, 2010, CIDU.

What is Circinus X-1?

Paul S. Ray, Berrie Giebels, Michael Paul Kowalski, 2002 .

Bayesian blocks: Wavelets and beyond

Jeffrey D. Scargle, J. Scargle, 2005, Integr. Comput. Aided Eng..

A Processing Automaton for Intensive Data

James P. Crutchfield, Jeffrey D. Scargle, C. Glymour, 2001 .

Wavelet methods in astronomical time series analysis

Jeffrey D. Scargle, J. Scargle, 1997 .

Estimating the instantaneous power spectrum of an x-ray binary system

Lorenzo Galleani, Leon Cohen, Jeffrey D. Scargle, 2001, SPIE Optics + Photonics.

Timing: New Methods for Astronomical Time Series Analysis

Jeffrey D. Scargle, 2000 .

Using Bayesian Blocks to Partition Self-Organizing Maps pdf

Jeffrey D. Scargle, Paul R. Gazis, P. Gazis, 2008, ArXiv.

Detection and modeling of chaotic dynamics using wavelet techniques

Jeffrey D. Scargle, J. Scargle, 1994, Defense, Security, and Sensing.

Studies in Astronomical Time Series Analysis: V. Bayesian Blocks, A New Method to Analyze Structure in

Jeffrey D. Scargle, 1998 .

Joint segmentation of multivariate Poissonian time series. Application to burst and transient source experiments

Jean-Yves Tourneret, Nicolas Dobigeon, Jeffrey D. Scargle, 2006, 2006 14th European Signal Processing Conference.

An introduction to chaotic and random time series analysis

Jeffrey D. Scargle, J. Scargle, 1989, Int. J. Imaging Syst. Technol..

Astronomical Time Series, Complexity in

Jeffrey D. Scargle, J. Scargle, 2009, Encyclopedia of Complexity and Systems Science.

Probability and Statistics in Astronomical Machine Learning and Data Mining

Jeffrey D. Scargle, J. Scargle, 2012 .

An algorithm for optimal partitioning of data on an interval

Jeffrey D. Scargle, Bradley W. Jackson, David Barnes, 2003, IEEE Signal Processing Letters.

PHASE-SENSITIVE DECONVOLUTION TO MODEL RANDOM PROCESSES, WITH SPECIAL REFERENCE TO ASTRONOMICAL DATA*

Jeffrey D. Scargle, 1981 .

Bayesian Blocks: Detecting and characterizing local variability in time series

Jeffrey D. Scargle, James Chiang, Jay P. Norris, 2012 .

BLOCK: A Bayesian block method to analyze structure in photon counting data

Jeffrey D. Scargle, 1999 .

The AISRP Code and Algorithm Library

Jeffrey D. Scargle, Paul R. Gazis, S. Hoban, 2006 .

Advances in Machine Learning and Data Mining for Astronomy

Ashok N. Srivastava, Jeffrey D. Scargle, Michael J. Way, 2012 .

Fractal-Based Point Processes

Jeffrey D. Scargle, J. Scargle, 2007, Technometrics.

Adaptive Impulse Noise Removal Using a Cost Function Based Optimal Partitioning

Jeffrey D. Scargle, Mahmoud K. Quweider, J. Scargle, 2008, 2008 Congress on Image and Signal Processing.

Joint Segmentation of Multivariate Astronomical Time Series: Bayesian Sampling With a Hierarchical Model

Jean-Yves Tourneret, Nicolas Dobigeon, Jeffrey D. Scargle, 2007, IEEE Transactions on Signal Processing.

Advanced Statistical and Data Analysis Tools for Astrophysics

Jeffrey D. Scargle, V. Kashyap, 2001 .

Nonlinear and Nonstationary Signal Processing

Jeffrey D. Scargle, J. Scargle, 2002, Technometrics.

Wavelet and Other Multi-resolution Methods for Time Series Analysis

Jeffrey D. Scargle, J. Scargle, 1997 .

An Introduction to the Theory of Point Processes, Vol. I: Elementary Theory and Methods

Jeffrey D. Scargle, J. Scargle, 2004, Technometrics.

Alanna Conners and the Origins of Principled Data Analysis

Jeffrey D. Scargle, 2013 .

Data Analysis Through Segmentation: Bayesian Blocks and Beyond

Jeffrey D. Scargle, 2004 .

Statistical challenges in modern astronomy II

Jeffrey D. Scargle, Jeffrey N. Cuzzi, J. Scargle, 1997 .

New Methods for Studying Periodic and Aperiodic Systems

Jeffrey D. Scargle, J. Scargle, 1997 .

Observation of X-Ray Variability in the BL Lacertae Object 1ES 1959+65

Paul S. Ray, Jeffrey D. Scargle, Berrie Giebels, 2002 .

Histogram Analysis of GALLEX, GNO, and SAGE Neutrino Data: Further Evidence for Variability of the Solar Neutrino Flux

Jeffrey D. Scargle, Peter A. Sturrock, J. Scargle, 2000, astro-ph/0011228.

Bayesian Estimation of Time Series Lags and Structure

Jeffrey D. Scargle, 2001 .

Machine Vision and Deep Learning for Classification of Radio SETI Signals

Jeffrey D. Scargle, Bron Nelson, Jon Richards, 2019, 1902.02426.

Collisionless damping of hydromagnetic waves in relativistic plasma. I. Weak Landau damping: heating of the Crab Nebula

Jeffrey D. Scargle, J. Scargle, A. Barnes, 1973 .

Publication Bias: The “File-Drawer” Problem in Scientific Inference

Jeffrey D. Scargle, J. Scargle, 2000 .

On the detection of other planetary systems by astrometric techniques

Jeffrey D. Scargle, D. C. Black, J. Scargle, 1982 .

Publication Bias %28 The "File-Drawer Problem"%29 in Scientific Inference

Jeffrey D. Scargle, Donald DeVincenzi, 1999 .

A Markov Chain Monte Carlo version of the genetic algorithm Differential Evolution : easy Bayesian computing for real parameter spaces

Jeffrey D. Scargle, Jay P. Norris, Brad Jackson, 2015 .