SAMSI Fall 2016 Astrostatistics

Lecture 12 (November 30)

• Student Presentations
  • Probabilistic Prediction Calibration Using Brier Score Zhenfeng Lin
  • Gaussian Process Regression with Application to Variable Star Classification Susheela Singh
  • Hierarchical Bayesian Modeling of Group Velocity Dispersions Charles Bonfield

Lecture 11 (November 16)

• Student Presentations
  • Review of "Modeling Light Curves for Improved Classification" Andy Lawler
  • Analysis of Unevenly Spaced Time Series Suman Chakraborty
  • Distributional Approximation of Regression M-Estimator Debraj Das
  • Genetic Algorithms and their use in Astronomy and R Code Kyle Roell

Lecture 10 (November 9)

• Some Statistical Aspects of Photometric Redshift Estimation

Lecture 9 (November 2)

• Guest Lecturer SAMSI Postdoc Hyungsuk Tak
  • Strong Lens Time Delay Estimation
  • Repelling-Attracting Metropolis Algorithm for Multimodality

Lecture 8 (October 26)

• Guest Lecturer Professor Jessi Cisewski: Approximate Bayesian Computation

Lecture 7 (October 19)

• Guest Lecturer Tom Loredo: Hierarchical Bayesian Modeling

Lecture 6 (October 12)

• Guest Lecturer Professor Viviana Acquaviva: Data Science Meets Astronomy: Galaxy Separation with Machine Learning

Lecture 5 (October 5)

• Regression with Measurement Error, Errors-in-Variables, Censoring, and Intrinsic Scatter
  • Lecture Slides
  • Background Reading: Some Aspects of Measurement Error in Linear Regression of Astronomical Data Kelly ApJ 2007

Lecture 4 (September 28)

• Guest Lecturer Professor Jogesh Babu: Model Fitting, Bootstrap, & Model Selection

Lecture 3 (September 21)

• Period Uncertainty Quantification, Modeling, Finding Structure in the Milky Way Halo

Lecture 2 (September 14)

• Distance Determination, Expansion of the Universe, and Statistical Issues
• Period Estimation for Variable Sources

Lecture 1 (September 7)

• Course Syllabus
• Introduction and Time Domain Astronomy
• Course Introduction and Time Domain Astronomy Background
• Building Automated Classifiers for Variable Sources
• Background Reading
• A Framework for Statistical Inference in Astrophysics by Schafer
• Data Mining and Machine-Learning in Time-Domain Discovery & Classification by Bloom and Richards
• Data Challenges of Time Domain Astronomy by Graham

---