| Paper | Title/topic | Notes |
|---|
| | |
| NEW - review of classification methods | a good starting point is sec. 9 of Ivezic et al. "Statistics, Data Mining, and Machine Learning in Astronomy" 2014 |
Cappellari and Copin 2003 | NEW - " Adaptive spatial binning of integral-field spectroscopic data using Voronoi tessellations" | lots of citations |
| NEW - Self Organizing Map | One the machine lerning methods. |
Leistedt et al. 2023 | "Hierarchical Bayesian inference of photometric redshifts with stellar population synthesis models" | |
Perren, Vázquez and Piatti 2015 | "ASteCA: Automated Stellar Cluster Analysis" | densities of clusters analyzed |
Weigel, Schawinski and Bruderer 2016 | "Stellar mass functions: methods, systematics and results for the local Universe" | STY maximum likelihood technique, SWML non-parametric maximum likelihood, and 1/Vmax; paper is relatively long |
Rojas-Arriagada et al. 2020 | "How many components? Quantifying the complexity of the metallicity distribution in the Milky Way bulge with APOGEE" | Gaussian mixture model and non-negative matrix factorization decomposition |
Camacho, Faria and Viana 2023 | "Modelling stellar activity with Gaussian process regression networks" | Gaussian processes and other methods |
Foreman-Mackey, Agol, Ambikasaran and Angus 2017 | "Fast and Scalable Gaussian Process Modeling with Applications to Astronomical Time Series" | see also https://github.com/dfm/celerite; more advanced version was already presented by M. Mróz |
| Tsutomou et al. 2022 | "High Dimensional Statistical Analysis and its Application to ALMA Map of NGC 253" | noise-reduction principal component analysis (NRPCA) and regularized principal component analysis (RPCA) used; suggested by Kasia Małek |
| Importance sampling | The method is very important for speeding-up simulations. Description of the method can be found on-line, or in resources suggested by RP. It would be good to show simple working example. |
| Speagle 2019 | "A Conceptual Introduction to Markov Chain Monte Carlo Methods" | Other concepts are also discussed: importance sampling, effective sample size, approximating posterior integrals with grids |
| Vitale 2020 | "One, No One, and One Hundred Thousand -- Inferring the properties of a population in presence of selection effects" | Pedagogical introduction to hierarchical Bayesian inference in presence of selection effects; gravitational waves as an example |
| Hogg and Forman-Mackey 2017 | "Data analysis recipes: Using Markov chain Monte Carlo" | Good MCMC introduction that has problems to be solved by the reader; discusses convergence, parametrizations etc. |
| LIGO and Virgo 2020 | "Population Properties of Compact Objects from the Second LIGO-Virgo Gravitational-Wave Transient Catalog" | gravitational waves - statistical analysis of O3 run that considers a few different models |
| Student project | Student can present what statistical analysis they work on, even if it's not finished. It's likely that other participants will give useful suggestions |
| Schwarzenberg-Czerny 1996 | "An astronomer's guide to period searching" | short |
| Zechmeister and Kurster 2009 | "The generalised Lomb-Scargle periodogram. A new formalism for the floating-mean and Keplerian periodograms" | |
| Date and speaker | Paper/Topic | Links |
|---|
| | |
| 20.11.2024, M. Kapusta | Simulation-Based Inference with Neural Posterior Estimation | slides |
| 6.11.2024, P. Mróz | Kammerer et al. 2020 "Increasing the achievable contrast of infrared interferometry with an error correlation model" | |
| 23.10.2024, R. Poleski | Introduction to neural networks | slides |
| | |
| 22.05.2024, J. Skowron | Karamanis et al. 2022"Accelerating astronomical and cosmological inference with preconditioned Monte Carlo" | slides; code at https://github.com/minaskar/pocomc |
| 8.05.2024, M. Kapusta | Rinaldi and Del Pozzo 2022"(H)DPGMM: a hierarchy of Dirichlet process Gaussian mixture models for the inference of the black hole mass function" | slides |
| 20.03.2024, R. Poleski | Ferguson and Strigari 2020 "Three-dimensional structure of the Sagittarius dwarf spheroidal core from RR Lyrae" | |
| 13.03.2024, R. Oliveira | Wen et al. 2024 "Hierarchical Bayesian inference of globular cluster properties" | slides |
| 23.01.2024, P. Roy | Romero-Shaw, Thrane, and Lasky 2022 "When models fail: An introduction to posterior predictive checks and model misspecification in gravitational-wave astronomy" | slides |
| 16.01.2024, D. Skowron | Wang, López-Corredoira and Wei 2023 "The Hubble Tension Survey: A Statistical Analysis of the 2012-2022 Measurements" | slides |
| 12.12.2023, R. Oliveira | Golovich et al. 2022 "A Reanalysis of Public Galactic Bulge Gravitational Microlensing Events from OGLE-III and -IV" | slides |
| 5.12.2023, M. Kiraga | Ensor et al. 2017 "A Principal Component Analysis of the Diffuse Interstellar Bands" | slides |
| 14.11.2023, M. Ban | Hsu, Ford, et al. 2018 "Improving the Accuracy of Planet Occurrence Rates from Kepler Using Approximate Bayesian Computation" | slides, animations |
| 31.10.2023, R. Poleski | Terry et al. 2022 "Adaptive Optics Imaging Can Break the Central Caustic Cusp Approach Degeneracy in High-magnification Microlensing Events" | slides |
| | |
| 25.05.2023, D. Skowron | Raphael Oliviera research | slides |
| 18.05.2023, P. Iwanek | Iwanek et al. 2023 "A Three-dimensional Map of the Milky Way Using 66,000 Mira Variable Stars" | slides |
| 20.04.2023, K. Iłkiewicz | GRAVITY Collaboration 2022 "Deep images of the Galactic center with GRAVITY" | slides |
| 6.04.2023, P. Szewczyk | Tekatsy et al. 2023 "What neutron stars tell about the hadron-quark phase transition: a Bayesian study" | slides |
| 23.03.2023, P. Mróz | Graham et al. 2013 "Using conditional entropy to identify periodicity" | |
| 9.03.2023, R. Poleski | Luri et al. 2018 "Gaia Data Release 2. Using Gaia parallaxes" | slides |
| 24.01.2023, M. Mróz | Gordon, Agol and Foreman-Mackey 2020 "A Fast, Two-dimensional Gaussian Process Method Based on Celerite: Applications to Transiting Exoplanet Discovery and Characterization" | slides |
| 20.12.2022, J. Skowron | Hogg, Bovy and Lang 2010 "Data analysis recipes: Fitting a model to data" - part 2 | slides |
| 6.12.2022, J. Skowron | Hogg, Bovy and Lang 2010 "Data analysis recipes: Fitting a model to data" - part 1 | slides |
| 22.11.2022, M. Ban | Koshimoto and Bennett 2020 "Evidence of Systematic Errors in Spitzer Microlens Parallax Measurements" | slides |
| 8.11.2022, R. Poleski | Hawkins et al. 2017 "Red clump stars and Gaia: calibration of the standard candle using a hierarchical probabilistic model" | slides |
| 21.10.2022, J. Skowron | Introduction to MCMC | |
| | |
| 9.06.2022, M. Kiraga | Salvato et al. 2018 "Finding counterparts for all-sky X-ray surveys with NWAY: a Bayesian algorithm for cross-matching multiple catalogues" | slides |
| 19.05.2022, M. Gromadzki | Kuhn and Feigelson 2017 "Mixture Models in Astronomy" | slides |
| 5.05.2022, K. Iłkiewicz | Francis and Wills 1999 "Introduction to Principal Components Analysis" | slides |
| 21.04.2022, M. Ban | Udalski et al. 2018 "OGLE-2017-BLG-1434Lb: Eighth q<1e-4 Mass-Ratio Microlens Planet Confirms Turnover in Planet Mass-Ratio Function" | slides |
| 31.03.2022, M. Ratajczak | Schwarzenberg-Czerny 1989 "On the advantage of using analysis of variance for period search." | slides |
| 17.03.2022, R. Poleski | Foreman-Mackey, Hogg and Morton 2014 "Exoplanet Population Inference and the Abundance of Earth Analogs from Noisy, Incomplete Catalogs" | slides |
| 3.03.2022, P. Mróz | Hogg, Myers, and Bovy 2010 "Inferring the Eccentricity Distribution" | notes |
| 11.01.2022, M. Jabłońska | Andrae 2010 "Error estimation in astronomy: A guide" | slides, github code |
| 14.12.2021, P. Szewczyk | Hogg 2008 "Data analysis recipes: Choosing the binning for a histogram" | slides, code |
| 30.11.2021, K. Kruszyńska | Bailer-Jones et al. 2021 " Estimating Distances from Parallaxes. V. Geometric and Photogeometric Distances to 1.47 Billion Stars in Gaia Early Data Release 3" | slides |
| 16.11.2021, J. Skowron | Introduction to MCMC - part 3 | |
| 9.11.2021, J. Skowron | Introduction to MCMC - part 2 | |
| 26.10.2021, J. Skowron | Introduction to MCMC - part 1 | |
| | |
| 11.6.2021, D. Skowron | VanderPlas and Ivezic 2015 "Periodograms for Multiband Astronomical Time Series" | slides | |
| 21.5.2021, M. Kiraga | Gould 2013 "The Most Precise Extra-Galactic Black-Hole Mass Measurement" | slides | |
| 14.5.2021, J. Skowron | UltraNest package - based on Buchner (2021) and Buchner (2021) | slides |
| 23.4.2021, Sz. Kozłowski | Quasar variability based on 1, 2, 3, and 4 | slides |
| 9.4.2021, K. Kruszyńska | "Bayesian Approach for Determining Microlens System Properties with High-angular-resolution Follow-up Imaging" Koshimoto, Bennett, and Suzuki 2020 | slides |
| 26.3.2021, R. Poleski | "Quantifying the Bayesian Evidence for a Planet in Radial Velocity Data" Nelson et al. 2020 | slides |
| 12.3.2021, M. Gromadzki | "Astrophysical Sources of Statistical Uncertainty in Precision Radial Velocities and Their Approximations" Beatty and Gaudi 2015 | slides |
| 14.1.2021, M. Ratajczak | "The Joker: A Custom Monte Carlo Sampler for Binary-star and Exoplanet Radial Velocity Data" Price-Whelan et al. 2017 | slides |
| 7.1.2021, P. Zieliński | "Detecting extrasolar planets from stellar radial velocities using Bayesian evidence" Feroz et al. 2011 | slides |
| 10.12.2020, R. Poleski | "Multimodal nested sampling: an efficient and robust alternative to Markov Chain Monte Carlo methods for astronomical data analyses" Feroz and Hobson 2008 | slides, code, data |
| 26.11.2020, Ł. Wyrzykowski | "Dos and don'ts of reduced chi-squared" Andrae et al. 2010 | slides |
| 19.11.2020, J. Skowron | Introduction to MCMC | slides, more slides, code 1, code 2, data |
| 29.10.2020, R. Poleski | Bayes' theorem and related concepts | slides |