Bayesian Models for Astrophysical Data: Using R, JAGS, Python, and Stan
暂无分享,去创建一个
Joseph Hilbe | Emille E. O. Ishida | Joseph M. Hilbe | Rafael S. de Souza | E. E. O. Ishida | E. Ishida | R. Souza | J. Hilbe | R. S. Souza | R. S. Souza
[1] S. Harju. Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and Stan. F. Korner‐Nievergelt, T. Roth, S. von Felten, J. Guélat, B. Almasi, and P. Korner‐Nievergelt. 2015. Elsevier, London, U.K. 316 pp. $76.39 paperback. ISBN 978‐0‐12‐801370‐0. , 2016 .
[2] Bruce A. Bassett,et al. Application of Bayesian graphs to SN Ia data analysis and compression , 2016, 1603.08519.
[3] Miguel de Val-Borro,et al. Is the cluster environment quenching the Seyfert activity in elliptical and spiral galaxies? , 2016, Monthly Notices of the Royal Astronomical Society.
[4] Robert C. Nichol,et al. SDSS-II SUPERNOVA SURVEY: AN ANALYSIS OF THE LARGEST SAMPLE OF TYPE IA SUPERNOVAE AND CORRELATIONS WITH HOST-GALAXY SPECTRAL PROPERTIES , 2016, 1602.02674.
[5] Michal Pawlak,et al. Period-luminosity-colour relation for early-type contact binaries , 2016, 1602.01467.
[6] B. Mallick. VARIABLE SELECTION FOR REGRESSION MODELS , 2016 .
[7] Richard McElreath,et al. Statistical Rethinking: A Bayesian Course with Examples in R and Stan , 2015 .
[8] A. Gelman,et al. Stan , 2015 .
[9] A. Herrero,et al. DISCOVERY OF THE MASSIVE OVERCONTACT BINARY VFTS 352: EVIDENCE FOR ENHANCED INTERNAL MIXING , 2015, 1509.08940.
[10] Klaus Dolag,et al. Weighted ABC: a new strategy for cluster strong lensing cosmology with simulations , 2015 .
[11] Joseph Hilbe,et al. Practical Guide to Logistic Regression , 2015 .
[12] Kyle Barbary,et al. UNITY: CONFRONTING SUPERNOVA COSMOLOGY’S STATISTICAL AND SYSTEMATIC UNCERTAINTIES IN A UNIFIED BAYESIAN FRAMEWORK , 2015, 1507.01602.
[13] R. S. de Souza,et al. The overlooked potential of generalized linear models in astronomy – III. Bayesian negative binomial regression and globular cluster populations , 2015, 1506.04792.
[14] Brian P. Weaver,et al. Bayesian Methods for the Physical Sciences: Learning from Examples in Astronomy and Physics , 2015 .
[15] Michael R. Blanton,et al. A STUDY IN BLUE: THE BARYON CONTENT OF ISOLATED LOW-MASS GALAXIES , 2015, 1505.04819.
[16] A. Amara,et al. Approximate Bayesian computation for forward modeling in cosmology , 2015, 1504.07245.
[17] E. E. O. Ishida,et al. cosmoabc: Likelihood-free inference via Population Monte Carlo Approximate Bayesian Computation , 2015, Astron. Comput..
[18] Pankaj Jain,et al. An Introduction to Astronomy and Astrophysics , 2015 .
[19] Shiro Ikeda,et al. Variable Selection for Modeling the Absolute Magnitude at Maximum of Type Ia Supernovae , 2015, 1504.01470.
[20] Rafael S. de Souza,et al. AMADA - Analysis of multidimensional astronomical datasets , 2015, Astron. Comput..
[21] Hilo,et al. THE ELEVENTH AND TWELFTH DATA RELEASES OF THE SLOAN DIGITAL SKY SURVEY: FINAL DATA FROM SDSS-III , 2015, 1501.00963.
[22] A. Krone-Martins,et al. The overlooked potential of Generalized Linear Models in astronomy-II: Gamma regression and photometric redshifts , 2014, Astron. Comput..
[23] R. S. de Souza,et al. The overlooked potential of Generalized Linear Models in astronomy, I: Binomial regression , 2014, Astron. Comput..
[24] D. Hathaway. The Solar Cycle , 2010, Living reviews in solar physics.
[25] Asis Kumar Chattopadhyay,et al. Statistical Methods for Astronomical Data Analysis , 2014 .
[26] Stefano Andreon,et al. Do cluster properties affect the quenching rate , 2014 .
[27] Adrian T. Lee,et al. GALAXY CLUSTERS DISCOVERED VIA THE SUNYAEV–ZEL'DOVICH EFFECT IN THE 2500-SQUARE-DEGREE SPT-SZ SURVEY , 2014, 1409.0850.
[28] Herbert I. Weisberg,et al. Willful Ignorance: The Mismeasure of Uncertainty , 2014 .
[29] J. Kruijssen,et al. Globular cluster formation in the context of galaxy formation and evolution , 2014, 1407.2953.
[30] A. C. Robin,et al. Constraining the thick disc formation scenario of the Milky Way , 2014, 1406.5384.
[31] Jocelyn E. Bolin,et al. Multilevel Modeling Using R , 2019 .
[32] Markus Janson,et al. THE ASTRALUX MULTIPLICITY SURVEY: EXTENSION TO LATE M-DWARFS , 2014, 1406.0535.
[33] Fabio Fontanot,et al. Variations of the initial mass function in semi-analytical models , 2014, 1405.7699.
[34] Chieh-An Lin,et al. A New Model to Predict Weak Lensing Peak Counts , 2014, Proceedings of the International Astronomical Union.
[35] M. Sullivan,et al. Improved cosmological constraints from a joint analysis of the SDSS-II and SNLS supernova samples , 2014, 1401.4064.
[36] George B. Lansbury,et al. Barred S0 galaxies in the Coma cluster , 2014, 1401.3775.
[37] Andrew J. Connolly,et al. Statistics, Data Mining, and Machine Learning in Astronomy , 2014 .
[38] Gregory B. Poole,et al. Globular clusters and supermassive black holes in galaxies: further analysis and a larger sample , 2013, 1312.5187.
[39] M. Penna-Lima,et al. Biases on cosmological parameter estimators from galaxy cluster number counts , 2013, 1312.4430.
[40] Filippo Mannucci,et al. Observational Clues to the Progenitors of Type Ia Supernovae , 2013, 1312.0628.
[41] R. Souza,et al. Robust PCA and MIC statistics of baryons in early minihaloes , 2013, 1308.6009.
[42] Aki Vehtari,et al. Understanding predictive information criteria for Bayesian models , 2013, Statistics and Computing.
[43] Michael Smithson,et al. Generalized Linear Models for Categorical and Continuous Limited Dependent Variables , 2013 .
[44] Tom Burr,et al. Selecting Summary Statistics in Approximate Bayesian Computation for Calibrating Stochastic Models , 2013, BioMed research international.
[45] William E. Harris,et al. A CATALOG OF GLOBULAR CLUSTER SYSTEMS: WHAT DETERMINES THE SIZE OF A GALAXY'S GLOBULAR CLUSTER POPULATION? , 2013, 1306.2247.
[46] Joseph M. Hilbe,et al. Methods of Statistical Model Estimation , 2013 .
[47] Richard F. Mushotzky,et al. THE FIRST HARD X-RAY POWER SPECTRAL DENSITY FUNCTIONS OF ACTIVE GALACTIC NUCLEUS , 2013, 1304.7002.
[48] L. Zaninetti,et al. THE INITIAL MASS FUNCTION MODELED BY A LEFT TRUNCATED BETA DISTRIBUTION , 2013, 1303.5597.
[49] Mary Kathryn Cowles,et al. Applied Bayesian Statistics: With R and OpenBUGS Examples , 2013 .
[50] K. Pimbblet,et al. The drivers of AGN activity in galaxy clusters: AGN fraction as a function of mass and environment , 2012, 1212.0261.
[51] Rafael S. de Souza,et al. Dark matter halo environment for primordial star formation , 2012, 1209.0825.
[52] W. M. Wood-Vasey,et al. LIKELIHOOD-FREE COSMOLOGICAL INFERENCE WITH TYPE Ia SUPERNOVAE: APPROXIMATE BAYESIAN COMPUTATION FOR A COMPLETE TREATMENT OF UNCERTAINTY , 2012, 1206.2563.
[53] Daniel Foreman-Mackey,et al. emcee: The MCMC Hammer , 2012, 1202.3665.
[54] E. Ishida,et al. Kernel PCA for Type Ia supernovae photometric classification , 2012, 1201.6676.
[55] S. Andreon. Understanding Better (Some) Astronomical Data Using Bayesian Methods , 2013 .
[56] David B. Dunson,et al. Bayesian data analysis, third edition , 2013 .
[57] A. Zuur,et al. A Beginner’s Guide to GLM and GLMM with R: A Frequentist and Bayesian Perspective for Ecologists , 2013 .
[58] James W. Hardin,et al. Modeling Underdispersed Count Data with Generalized Poisson Regression , 2012 .
[59] A. Futschik,et al. A Novel Approach for Choosing Summary Statistics in Approximate Bayesian Computation , 2012, Genetics.
[60] Katherine L. Rhode,et al. EXPLORING THE CORRELATIONS BETWEEN GLOBULAR CLUSTER POPULATIONS AND SUPERMASSIVE BLACK HOLES IN GIANT GALAXIES , 2012, 1210.4570.
[61] David J. Lunn,et al. The BUGS Book: A Practical Introduction to Bayesian Analysis , 2013 .
[62] G. Jogesh Babu,et al. Modern Statistical Methods for Astronomy: With R Applications , 2012 .
[63] C. Evans,et al. Binary Interaction Dominates the Evolution of Massive Stars , 2012, Science.
[64] J. Hardin,et al. Generalized Linear Models and Extensions, Third Edition , 2012 .
[65] Stefano Borgani,et al. Formation of Galaxy Clusters , 2012, 1205.5556.
[66] Andrew C. Fabian,et al. Observational Evidence of Active Galactic Nuclei Feedback , 2012 .
[67] Surajit Chattopadhyay,et al. Monthly sunspot number time series analysis and its modeling through autoregressive artificial neural network , 2012, The European Physical Journal Plus.
[68] A. N. Pettitt,et al. Approximate Bayesian Computation for astronomical model analysis: a case study in galaxy demographics and morphological transformation at high redshift , 2012, 1202.1426.
[69] Aki Vehtari,et al. A survey of Bayesian predictive methods for model assessment, selection and comparison , 2012 .
[70] Joseph M. Hilbe,et al. Modeling Count Data , 2014, International Encyclopedia of Statistical Science.
[71] K. Ulaczyk,et al. The Optical Gravitational Lensing Experiment. The OGLE-III Catalog of Variable Stars. XII. Eclipsing Binary Stars in the Large Magellanic Cloud , 2011, 1108.0446.
[72] M. Blanton,et al. IMPROVED BACKGROUND SUBTRACTION FOR THE SLOAN DIGITAL SKY SURVEY IMAGES , 2011, 1105.1960.
[73] A. Marconi,et al. The role of secular evolution in the black hole growth of narrow-line Seyfert 1 galaxies , 2011, 1104.5023.
[74] Paul Teetor,et al. R Cookbook , 2011 .
[75] Gregory F. Snyder,et al. RELATION BETWEEN GLOBULAR CLUSTERS AND SUPERMASSIVE BLACK HOLES IN ELLIPTICALS AS A MANIFESTATION OF THE BLACK HOLE FUNDAMENTAL PLANE , 2011, 1101.1299.
[76] Ewan Cameron,et al. On the Estimation of Confidence Intervals for Binomial Population Proportions in Astronomy: The Simplicity and Superiority of the Bayesian Approach , 2010, Publications of the Astronomical Society of Australia.
[77] Gautham Narayan,et al. TYPE Ia SUPERNOVA LIGHT CURVE INFERENCE: HIERARCHICAL MODELS IN THE OPTICAL AND NEAR-INFRARED , 2010, 1011.5910.
[78] M. Sullivan,et al. SUPERNOVA CONSTRAINTS AND SYSTEMATIC UNCERTAINTIES FROM THE FIRST THREE YEARS OF THE SUPERNOVA LEGACY SURVEY , 2011, 1104.1443.
[79] S. Khochfar,et al. The interplay between chemical and mechanical feedback from the first generation of stars , 2010, 1011.3999.
[80] Matt P. Wand,et al. Non-Standard Semiparametric Regression via BRugs , 2010 .
[81] William E. Harris,et al. The globular cluster/central black hole connection in galaxies , 2010, 1008.4748.
[82] N. S. Philip,et al. Results from the Supernova Photometric Classification Challenge , 2010, 1008.1024.
[83] Yu Wang,et al. Internal properties and environments of dark matter haloes , 2010, 1007.0612.
[84] Andrew J. Benson,et al. Galaxy formation theory , 2010, 1006.5394.
[85] Andreas Burkert,et al. A CORRELATION BETWEEN CENTRAL SUPERMASSIVE BLACK HOLES AND THE GLOBULAR CLUSTER SYSTEMS OF EARLY-TYPE GALAXIES , 2010, 1004.0137.
[86] B. Ciardi,et al. The transition from population III to population II-I star formation , 2010, 1003.4992.
[87] R. O’Hara,et al. Do not log‐transform count data , 2010 .
[88] S. Andreon,et al. The scaling relation between richness and mass of galaxy clusters: a Bayesian approach , 2010, 1001.4639.
[89] Cambridge,et al. A Universal Stellar Initial Mass Function? A critical look at variations in extreme environments , 2010, 1001.2965.
[90] C. Lintott,et al. Galaxy Zoo: Passive Red Spirals . , 2009, 0910.4113.
[91] Joseph Hilbe,et al. R for Stata Users , 2010 .
[92] L. Cortese,et al. Evolutionary paths to and from the red sequence: star formation and H i properties of transition galaxies at z∼ 0 , 2009, 0908.3564.
[93] S. Mahajan,et al. Red star-forming and blue passive galaxies in clusters , 2009, 0908.2434.
[94] H. Rue,et al. Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations , 2009 .
[95] R. O’Hara,et al. A review of Bayesian variable selection methods: what, how and which , 2009 .
[96] K. Abazajian,et al. THE SEVENTH DATA RELEASE OF THE SLOAN DIGITAL SKY SURVEY , 2008, 0812.0649.
[97] G. Zamorani,et al. The zCOSMOS redshift survey: the three-dimensional classification cube and bimodality in galaxy physical properties , 2008, 0810.2245.
[98] C. Robert,et al. Adaptive approximate Bayesian computation , 2008, 0805.2256.
[99] P. Hopkins,et al. A semi-analytic model for the co-evolution of galaxies, black holes and active galactic nuclei , 2008, 0808.1227.
[100] Bradley M. Peterson,et al. The central black hole and relationships with the host galaxy , 2008 .
[101] G. Casella,et al. The Bayesian Lasso , 2008 .
[102] Alexander S. Szalay,et al. Galaxy Zoo: the dependence of morphology and colour on environment , 2008, 0805.2612.
[103] C. Lintott,et al. Galaxy Zoo: morphologies derived from visual inspection of galaxies from the Sloan Digital Sky Survey , 2008, 0804.4483.
[104] Shu-ichiro Inutsuka,et al. Formation Scenario for Wide and Close Binary Systems , 2007, 0709.2739.
[105] Calyampudi Radhakrishna Rao,et al. Epidemiology and medical statistics , 2008 .
[106] W. M. Wood-Vasey,et al. SDSS-III: MASSIVE SPECTROSCOPIC SURVEYS OF THE DISTANT UNIVERSE, THE MILKY WAY, AND EXTRA-SOLAR PLANETARY SYSTEMS , 2011, 1101.1529.
[107] K. Schawinski,et al. Observational evidence for AGN feedback in early-type galaxies , 2007, 0709.3015.
[108] B. Kelly. Some Aspects of Measurement Error in Linear Regression of Astronomical Data , 2007, 0705.2774.
[109] F. Feroz,et al. Multimodal nested sampling: an efficient and robust alternative to Markov Chain Monte Carlo methods for astronomical data analyses , 2007, 0704.3704.
[110] J. Vaquero. Historical sunspot observations: A review , 2007, astro-ph/0702068.
[111] M. Sullivan,et al. SALT2: using distant supernovae to improve the use of type Ia supernovae as distance indicators , 2007, astro-ph/0701828.
[112] Oliver Hahn,et al. Properties of dark matter haloes in clusters, filaments, sheets and voids , 2006, astro-ph/0610280.
[113] B. Moore,et al. Concentration, spin and shape of dark matter haloes: Scatter and the dependence on mass and environment , 2006, astro-ph/0608157.
[114] Astronomy,et al. The spin and shape of dark matter haloes in the Millennium simulation of a Λ cold dark matter universe , 2006, astro-ph/0608607.
[115] Dani Gamerman,et al. Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference, Second Edition , 2006 .
[116] S. Littlefair,et al. Circumstellar discs around solar mass stars in NGC 6611 , 2005, astro-ph/0501208.
[117] A. Gelman. Prior distributions for variance parameters in hierarchical models (comment on article by Browne and Draper) , 2004 .
[118] R. Wilson. Modern Cosmology , 2004 .
[119] Timothy M. Heckman,et al. The host galaxies of active galactic nuclei , 2003 .
[120] G. Chabrier. Galactic Stellar and Substellar Initial Mass Function , 2003, astro-ph/0304382.
[121] P. McCullagh. What is a statistical model , 2002 .
[122] Bradley P. Carlin,et al. Bayesian measures of model complexity and fit , 2002 .
[123] V. Kashyap,et al. Flare Heating in Stellar Coronae , 2002, astro-ph/0208546.
[124] S. Tremaine,et al. The Slope of the Black Hole Mass versus Velocity Dispersion Correlation , 2002, astro-ph/0203468.
[125] P. Uttley,et al. Measuring the broad-band power spectra of active galactic nuclei with RXTE , 2002, astro-ph/0201134.
[126] L. Kewley,et al. Theoretical Modeling of Starburst Galaxies , 2001, astro-ph/0106324.
[127] J. Hardin,et al. Generalized Linear Models and Extensions , 2001 .
[128] L. Hernquist,et al. First Structure Formation: A Simulation of Small-Scale Structure at High Redshift , 2000, astro-ph/0009254.
[129] P. Kroupa. On the variation of the initial mass function , 2000, astro-ph/0009005.
[130] D. Merritt,et al. Black Hole Demographics from the M(BH)-sigma Relation , 2000, astro-ph/0009076.
[131] Ralf Bender,et al. Black Hole Mass Estimates from Reverberation Mapping and from Spatially Resolved Kinematics , 2000, astro-ph/0007123.
[132] W. Hillebrandt,et al. Type IA Supernova Explosion Models , 2000, astro-ph/0006305.
[133] D. Merritt,et al. A Fundamental Relation between Supermassive Black Holes and Their Host Galaxies , 2000, astro-ph/0006053.
[134] Mireille Louys,et al. The ALADIN interactive sky atlas - A reference tool for identification of astronomical sources , 2000 .
[135] I. Hook,et al. Measurements of Ω and Λ from 42 High-Redshift Supernovae , 1998, astro-ph/9812133.
[136] A. Riess,et al. Observational Evidence from Supernovae for an Accelerating Universe and a Cosmological Constant , 1998, astro-ph/9805201.
[137] Gutti Jogesh Babu,et al. Statistical Challenges in Modern Astronomy IV , 1998 .
[138] R. Tibshirani. Regression Shrinkage and Selection via the Lasso , 1996 .
[139] David B. Dunson,et al. Bayesian Data Analysis , 2010 .
[140] Jeff Harrison,et al. Applied Bayesian Forecasting and Time Series Analysis , 1994 .
[141] S Richardson,et al. A Bayesian approach to measurement error problems in epidemiology using conditional independence models. , 1993, American journal of epidemiology.
[142] E. Feigelson,et al. Statistical Challenges in Modern Astronomy , 2004, astro-ph/0401404.
[143] F. Famoye,et al. Generalized poisson regression model , 1992 .
[144] S. E. Hills,et al. Illustration of Bayesian Inference in Normal Data Models Using Gibbs Sampling , 1990 .
[145] G. J. Babu,et al. Linear regression in astronomy. II , 1990 .
[146] Adrian F. M. Smith,et al. Sampling-Based Approaches to Calculating Marginal Densities , 1990 .
[147] C. N. Morris,et al. The calculation of posterior distributions by data augmentation , 1987 .
[148] S. Djorgovski,et al. Fundamental Properties of Elliptical Galaxies , 1987 .
[149] D. Rubin. Bayesianly Justifiable and Relevant Frequency Calculations for the Applied Statistician , 1984 .
[150] Donald Geman,et al. Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[151] J. Baldwin,et al. ERRATUM - CLASSIFICATION PARAMETERS FOR THE EMISSION-LINE SPECTRA OF EXTRAGALACTIC OBJECTS , 1981 .
[152] G. Schwarz. Estimating the Dimension of a Model , 1978 .
[153] H. Akaike. A new look at the statistical model identification , 1974 .
[154] Y. Zeldovich,et al. The Observations of relic radiation as a test of the nature of X-Ray radiation from the clusters of galaxies , 1972 .
[155] W. K. Hastings,et al. Monte Carlo Sampling Methods Using Markov Chains and Their Applications , 1970 .
[156] D. Lynden-Bell,et al. Galactic Nuclei as Collapsed Old Quasars , 1969, Nature.
[157] E. Salpeter. The Luminosity function and stellar evolution , 1955 .
[158] N. Metropolis,et al. Equation of State Calculations by Fast Computing Machines , 1953, Resonance.
[159] N. Metropolis,et al. The Monte Carlo method. , 1949, Journal of the American Statistical Association.