Nested sampling for physical scientists

[1]  Will Handley,et al.  Nested Sampling for Frequentist Computation: Fast Estimation of Small p-Values. , 2021, Physical review letters.

[2]  Jonathan M. Cornell,et al.  Simple and statistically sound recommendations for analysing physical theories , 2020, Reports on progress in physics. Physical Society.

[3]  B. Brewer.  Nested Sampling , 2022, The SAGE Encyclopedia of Research Design.

[4]  P. K. Panda,et al.  GWTC-3: Compact Binary Coalescences Observed by LIGO and Virgo During the Second Part of the Third Observing Run , 2021, 2111.03606.

[5]  Do Kester,et al.  BayesicFitting, a PYTHON toolbox for Bayesian fitting and evidence calculation.: Including a Nested Sampling implementation , 2021, Astron. Comput..

[6]  Volker L. Deringer,et al.  Gaussian Process Regression for Materials and Molecules , 2021, Chemical reviews.

[7]  Gábor Csányi,et al.  Nested sampling for materials , 2021, The European Physical Journal B.

[8]  T. Callister.  A Thesaurus for Common Priors in Gravitational-Wave Astronomy , 2021, 2104.09508.

[9]  W. Handley,et al.  Constraining quantum initial conditions before inflation , 2021, Physical Review D.

[10]  Sylvain Le Corff,et al.  NEO: Non Equilibrium Sampling on the Orbit of a Deterministic Transform , 2021, 2103.10943.

[11]  A. Bartók,et al.  Insight into Liquid Polymorphism from the Complex Phase Behavior of a Simple Model. , 2021, Physical review letters.

[12]  Will Handley,et al.  Nested sampling with any prior you like , 2021, 2102.12478.

[13]  M. Hobson,et al.  Bayesian evidence for the tensor-to-scalar ratio r and neutrino masses mν : Effects of uniform versus logarithmic priors , 2021, 2102.11511.

[14]  C. Messenger,et al.  Nested sampling with normalizing flows for gravitational-wave inference , 2021, Physical Review D.

[15]  Johannes Buchner.  Nested Sampling Methods , 2021 .

[16]  J. Buchner.  UltraNest - a robust, general purpose Bayesian inference engine , 2021, J. Open Source Softw..

[17]  Marina Vannucci,et al.  Bayesian statistics and modelling , 2020, Nature Reviews Methods Primers.

[18]  Eduardo C. Garrido-Merch'an,et al.  A comparison of optimisation algorithms for high-dimensional particle and astrophysics applications , 2021, Journal of High Energy Physics.

[19]  T. Littenberg,et al.  BayesWave analysis pipeline in the era of gravitational wave observations , 2020, Physical Review D.

[20]  Will Handley,et al.  Nested sampling with plateaus , 2021 .

[21]  Will Handley,et al.  A general Bayesian framework for foreground modelling and chromaticity correction for global 21 cm experiments , 2020, Monthly Notices of the Royal Astronomical Society.

[22]  M. White,et al.  CosmoBit: a GAMBIT module for computing cosmological observables and likelihoods , 2020, Journal of Cosmology and Astroparticle Physics.

[23]  H. Hoekstra,et al.  KiDS-1000 cosmology: Cosmic shear constraints and comparison between two point statistics , 2020, Astronomy & Astrophysics.

[24]  Conrad W. Rosenbrock,et al.  Machine-learned interatomic potentials for alloys and alloy phase diagrams , 2019, npj Computational Materials.

[25]  J. Laskar,et al.  The HARPS search for southern extra-solar planets - XXXIV. A planetary system around the nearby M dwarf GJ 163, with a super-Earth possibly in the habitable zone , 2013, 1306.0904.

[26]  JAXNS: a high-performance nested sampling package based on JAX , 2020, 2012.15286.

[27]  Mustafa Khammash,et al.  Likelihood-free nested sampling for parameter inference of biochemical reaction networks , 2020, PLoS Comput. Biol..

[28]  M. White,et al.  Strengthening the bound on the mass of the lightest neutrino with terrestrial and cosmological experiments , 2020, 2009.03287.

[29]  G. Ashton,et al.  Massively parallel Bayesian inference for transient gravitational-wave astronomy , 2020, Monthly Notices of the Royal Astronomical Society.

[30]  Jaime Fern'andez del R'io,et al.  Array programming with NumPy , 2020, Nature.

[31]  L. Pártay,et al.  Pressure-Temperature Phase Diagram of Lithium, Predicted by Embedded Atom Model Potentials. , 2020, The journal of physical chemistry. B.

[32]  Will Handley,et al.  Nested sampling cross-checks using order statistics , 2020, 2006.03371.

[33]  M. J. Williams,et al.  Bayesian inference for compact binary coalescences with bilby: validation and application to the first LIGO–Virgo gravitational-wave transient catalogue , 2020, Monthly Notices of the Royal Astronomical Society.

[34]  Nested Sampling And Likelihood Plateaus , 2020, 2005.08602.

[35]  F. Llorente,et al.  Marginal likelihood computation for model selection and hypothesis testing: an extensive review , 2020, SIAM Rev..

[36]  Christian P. Robert,et al.  Computing Bayes: Bayesian Computation from 1763 to the 21st Century , 2020, 2004.06425.

[37]  Bart de Boer,et al.  Detrending the Waveforms of Steady-State Vowels , 2020, Entropy.

[38]  M. Trassinelli,et al.  Mean Shift Cluster Recognition Method Implementation in the Nested Sampling Algorithm , 2020, Entropy.

[39]  D. Apai,et al.  Helios-r2: A New Bayesian, Open-source Retrieval Model for Brown Dwarfs and Exoplanet Atmospheres , 2019, The Astrophysical Journal.

[40]  Robert W. Taylor,et al.  Model comparison from LIGO-Virgo data on GW170817's binary components and consequences for the merger remnant , 2019, 1908.01012.

[41]  Joel Nothman,et al.  SciPy 1.0-Fundamental Algorithms for Scientific Computing in Python , 2019, ArXiv.

[42]  H. Hoekstra,et al.  KiDS+VIKING-450 and DES-Y1 combined: Cosmology with cosmic shear , 2019, Astronomy & Astrophysics.

[43]  J. Speagle.  dynesty: a dynamic nested sampling package for estimating Bayesian posteriors and evidences , 2019, Monthly Notices of the Royal Astronomical Society.

[44]  A. Moss.  Accelerated Bayesian inference using deep learning , 2019, Monthly Notices of the Royal Astronomical Society.

[45]  O. Dor'e,et al.  Neutrino puzzle: Anomalies, interactions, and cosmological tensions , 2019, Physical Review D.

[46]  Jonathan M. Cornell,et al.  Simple and statistically sound strategies for analysing physical theories , 2020 .

[47]  D. Wales,et al.  Nested basin-sampling. , 2019, Journal of chemical theory and computation.

[48]  Antony Lewis,et al.  GetDist: a Python package for analysing Monte Carlo samples , 2019, 1910.13970.

[49]  J. Bozek,et al.  Assessing the Surface Oxidation State of Free-Standing Gold Nanoparticles Produced by Laser Ablation. , 2019, Langmuir : the ACS journal of surfaces and colloids.

[50]  Will Handley.  Curvature tension: Evidence for a closed universe , 2019, 1908.09139.

[51]  Farhan Feroz,et al.  Bayesian automated posterior repartitioning for nested sampling , 2019, ArXiv.

[52]  M. Hobson,et al.  Bayesian inflationary reconstructions from Planck 2018 data , 2019, Physical Review D.

[53]  M. Trassinelli.  The Nested_fit Data Analysis Program , 2019, Proceedings.

[54]  L. Baiotti.  Gravitational waves from neutron star mergers and their relation to the nuclear equation of state , 2019, Progress in Particle and Nuclear Physics.

[55]  M. S. Sanjari,et al.  New test of modulated electron capture decay of hydrogen-like 142Pm ions: Precision measurement of purely exponential decay , 2019, Physics Letters B.

[56]  Will Handley,et al.  Anesthetic: Nested Sampling Visualisation , 2019, J. Open Source Softw..

[57]  B. A. Boom,et al.  Searches for Gravitational Waves from Known Pulsars at Two Harmonics in 2015–2017 LIGO Data , 2019, The Astrophysical Journal.

[58]  Will Handley,et al.  Quantifying tensions in cosmological parameters: Interpreting the DES evidence ratio , 2019, Physical Review D.

[59]  Eric Vanden-Eijnden,et al.  Dynamical Computation of the Density of States and Bayes Factors Using Nonequilibrium Importance Sampling. , 2019, Physical review letters.

[60]  A. Lasenby,et al.  Constraining the kinetically dominated universe , 2018, Physical Review D.

[61]  Colm Talbot,et al.  An introduction to Bayesian inference in gravitational-wave astronomy: Parameter estimation, model selection, and hierarchical models , 2018, Publications of the Astronomical Society of Australia.

[62]  Xi Chen,et al.  Improving the efficiency and robustness of nested sampling using posterior repartitioning , 2018, Statistics and Computing.

[63]  Johannes Buchner,et al.  Collaborative Nested Sampling: Big Data versus Complex Physical Models , 2017, Publications of the Astronomical Society of the Pacific.

[64]  Edward Higson,et al.  Dynamic nested sampling: an improved algorithm for parameter estimation and evidence calculation , 2017, Statistics and Computing.

[65]  R. Bouckaert,et al.  Model Selection and Parameter Inference in Phylogenetics Using Nested Sampling , 2017, Systematic biology.

[66]  M. P. Hobson,et al.  Importance Nested Sampling and the MultiNest Algorithm , 2013, The Open Journal of Astrophysics.

[67]  T. E. Riley.  Neutron star parameter estimation from a NICER perspective , 2019 .

[68]  Edward Higson,et al.  dyPolyChord: dynamic nested sampling with PolyChord , 2018, J. Open Source Softw..

[69]  Edward Higson,et al.  Nestcheck: Error Analysis, Diagnostic Tests and Plots for Nested Sampling Calculations , 2018, J. Open Source Softw..

[70]  Edward Higson,et al.  Bayesian sparse reconstruction: a brute-force approach to astronomical imaging and machine learning , 2018, Monthly Notices of the Royal Astronomical Society.

[71]  P. Bolhuis,et al.  UvA-DARE (Digital Academic Repository) Nested Transition Path Sampling , 2018 .

[72]  Samantha J. Thompson,et al.  On the Feasibility of Intense Radial Velocity Surveys for Earth-Twin Discoveries , 2018, Monthly Notices of the Royal Astronomical Society.

[73]  Dirk P. Kroese,et al.  Unbiased and consistent nested sampling via sequential Monte Carlo , 2018, 1805.03924.

[74]  Aki Vehtari,et al.  Validating Bayesian Inference Algorithms with Simulation-Based Calibration , 2018, 1804.06788.

[75]  E. Mátyus,et al.  Direct Computation of the Quantum Partition Function by Path-Integral Nested Sampling. , 2018, Journal of chemical theory and computation.

[76]  M. Hobson,et al.  nestcheck: diagnostic tests for nested sampling calculations , 2018, Monthly Notices of the Royal Astronomical Society.

[77]  L. Pártay.  On the performance of interatomic potential models of iron: Comparison of the phase diagrams , 2018, Computational Materials Science.

[78]  José Paulo Santos,et al.  High-precision measurements of n = 2 → n = 1 transition energies and level widths in He- and Be- like Argon Ions , 2018, 1802.05970.

[79]  H. Gorke,et al.  Line shape analysis of the Kβ transition in muonic hydrogen , 2017, 1709.05950.

[80]  B. Yanny,et al.  Dark Energy Survey year 1 results: Cosmological constraints from galaxy clustering and weak lensing , 2017, Physical Review D.

[81]  M. Hobson,et al.  Sampling Errors in Nested Sampling Parameter Estimation , 2017, Bayesian Analysis.

[82]  Wolfgang von der Linden,et al.  Nested sampling, statistical physics and the Potts model , 2018, J. Comput. Phys..

[83]  Rory Smith,et al.  Optimal Search for an Astrophysical Gravitational-Wave Background , 2017, 1712.00688.

[84]  Lei Cao,et al.  Combined-chain nested sampling for efficient Bayesian model comparison , 2017, Digit. Signal Process..

[85]  Gábor Csányi,et al.  Constant-pressure nested sampling with atomistic dynamics. , 2017, Physical review. E.

[86]  Daniel Foreman-Mackey,et al.  Data Analysis Recipes: Using Markov Chain Monte Carlo , 2017, 1710.06068.

[87]  Ning Xiang,et al.  Room acoustic modal analysis using Bayesian inference. , 2017, The Journal of the Acoustical Society of America.

[88]  I. Hudson,et al.  New prior sampling methods for nested sampling - Development and testing , 2017 .

[89]  Coryn A. L. Bailer-Jones,et al.  Practical Bayesian Inference: A Primer for Physical Scientists , 2017 .

[90]  M. Pitkin,et al.  A nested sampling code for targeted searches for continuous gravitational waves from pulsars , 2017, 1705.08978.

[91]  J. Conrad,et al.  Comparison of statistical sampling methods with ScannerBit, the GAMBIT scanning module , 2017, The European Physical Journal C.

[92]  Thomas A. Severini,et al.  Integrated likelihood computation methods , 2017, Comput. Stat..

[93]  R. Nichol,et al.  Dynamical dark energy in light of the latest observations , 2017, Nature Astronomy.

[94]  Michael Betancourt,et al.  A Conceptual Introduction to Hamiltonian Monte Carlo , 2017, 1701.02434.

[95]  A. Lasenby,et al.  Constraining the dark energy equation of state using Bayes theorem and the Kullback–Leibler divergence , 2016, 1607.00270.

[96]  Clément Walter,et al.  Point process-based Monte Carlo estimation , 2014, Stat. Comput..

[97]  Martino Trassinelli,et al.  Bayesian data analysis tools for atomic physics , 2016, 1611.10189.

[98]  Ik Siong Heng,et al.  Inferring the core-collapse supernova explosion mechanism with gravitational waves , 2016, 1610.05573.

[99]  M. Bonnefoy,et al.  HELIOS–RETRIEVAL: An Open-source, Nested Sampling Atmospheric Retrieval Code; Application to the HR 8799 Exoplanets and Inferred Constraints for Planet Formation , 2016, 1610.03216.

[100]  Sebastian Bocquet,et al.  pygtc: beautiful parameter covariance plots (aka. Giant Triangle Confusograms) , 2016, J. Open Source Softw..

[101]  J. López‐Pavón,et al.  Testable baryogenesis in seesaw models , 2016, 1606.06719.

[102]  D Huet,et al.  GW151226: Observation of Gravitational Waves from a 22-Solar-Mass Binary Black Hole Coalescence , 2016 .

[103]  Daniel Foreman-Mackey,et al.  DNest4: Diffusive Nested Sampling in C++ and Python , 2016, 1606.03757.

[104]  Daniel Foreman-Mackey,et al.  corner.py: Scatterplot matrices in Python , 2016, J. Open Source Softw..

[105]  Luciano Rezzolla,et al.  Extraction of gravitational waves in numerical relativity , 2016, Living reviews in relativity.

[106]  J. Egger,et al.  Measurement of the charged pion mass using X-ray spectroscopy of exotic atoms , 2016, 1605.03300.

[107]  Erik Schultes,et al.  The FAIR Guiding Principles for scientific data management and stewardship , 2016, Scientific Data.

[108]  Andrew Fowlie,et al.  Superplot: a graphical interface for plotting and analysing MultiNest output , 2016, The European Physical Journal Plus.

[109]  The Ligo Scientific Collaboration,et al.  Observation of Gravitational Waves from a Binary Black Hole Merger , 2016, 1602.03837.

[110]  Erik Katsavounidis,et al.  Information-theoretic approach to the gravitational-wave burst detection problem , 2015, 1511.05955.

[111]  M. Payne,et al.  Determining pressure-temperature phase diagrams of materials , 2015, 1503.03404.

[112]  Johannes Buchner,et al.  A statistical test for Nested Sampling algorithms , 2014, Statistics and Computing.

[113]  Equidistribution testing with Bayes factors and the ECT , 2016 .

[114]  B. Wilson,et al.  Nested sampling of isobaric phase space for the direct evaluation of the isothermal-isobaric partition function of atomic systems. , 2015, The Journal of chemical physics.

[115]  A. D. L. Fortelle.  A study on generalized inverses and increasing functions Part I: generalized inverses , 2015 .

[116]  Joris De Ridder,et al.  DIAMONDS: a new Bayesian nested sampling tool , 2015, 1509.08311.

[117]  A. Lasenby,et al.  polychord: next-generation nested sampling , 2015, 1506.00171.

[118]  J. Marrouche,et al.  The pMSSM10 after LHC run 1 , 2015, The European physical journal. C, Particles and fields.

[119]  M. Habeck.  Nested sampling with demons , 2015 .

[120]  G. W. Pratt,et al.  Planck 2015. XX. Constraints on inflation , 2015, 1502.02114.

[121]  M. P. Hobson,et al.  polychord: nested sampling for cosmology , 2015, Monthly Notices of the Royal Astronomical Society: Letters.

[122]  Brendon J. Brewer,et al.  Fast Bayesian inference for exoplanet discovery in radial velocity data , 2015 .

[123]  Michael Habeck,et al.  Bayesian evidence and model selection , 2014, Digit. Signal Process..

[124]  M. S. Shahriar,et al.  Characterization of the LIGO detectors during their sixth science run , 2014, 1410.7764.

[125]  P. Graff,et al.  Parameter estimation for compact binaries with ground-based gravitational-wave observations using the LALInference software library , 2014, 1409.7215.

[126]  S. Klimenko,et al.  Advanced LIGO , 2014, 1411.4547.

[127]  Paul M. Goggans,et al.  Parallelized nested sampling , 2014 .

[128]  James G. Scott,et al.  Vertical-likelihood Monte Carlo , 2014, 1409.3601.

[129]  C. Broeck,et al.  Advanced Virgo: a second-generation interferometric gravitational wave detector , 2014, 1408.3978.

[130]  Wolfgang von der Linden,et al.  Bayesian Probability Theory: Applications in the Physical Sciences , 2014 .

[131]  D. Ireland,et al.  Development of Bayesian analysis program for extraction of polarisation observables at CLAS , 2014 .

[132]  R. Catena,et al.  Global fits of the dark matter-nucleon effective interactions , 2014, 1405.2637.

[133]  D. Frenkel,et al.  Superposition Enhanced Nested Sampling , 2014, Physical Review X.

[134]  Richard J. Morris,et al.  Bayesian Model Comparison and Parameter Inference in Systems Biology Using Nested Sampling , 2014, PloS one.

[135]  A. Roeck,et al.  The CMSSM and NUHM1 after LHC Run 1 , 2014, The European Physical Journal C.

[136]  M. Hannam.  Modelling gravitational waves from precessing black-hole binaries: progress, challenges and prospects , 2013, General Relativity and Gravitation.

[137]  J. Dunkley,et al.  Comparison of sampling techniques for Bayesian parameter estimation , 2013, 1308.2675.

[138]  C. A. Oxborrow,et al.  Planck 2015 results. I. Overview of products and scientific results , 2015 .

[139]  Gábor Csányi,et al.  Nested sampling for materials: the case of hard spheres. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

[140]  Luc Blanchet,et al.  Gravitational Radiation from Post-Newtonian Sources and Inspiralling Compact Binaries , 2002, Living reviews in relativity.

[141]  Mary F. Wheeler,et al.  Nested sampling algorithm for subsurface flow model selection, uncertainty quantification, and nonlinear calibration , 2013 .

[142]  David J. Wales,et al.  Surveying a complex potential energy landscape: Overcoming broken ergodicity using basin-sampling , 2013 .

[143]  L. Roszkowski,et al.  Dark matter and collider signatures of the MSSM , 2013, 1306.1567.

[144]  Frederik Beaujean,et al.  Initializing adaptive importance sampling with Markov chains , 2013, 1304.7808.

[145]  Paul Embrechts,et al.  A note on generalized inverses , 2013, Math. Methods Oper. Res..

[146]  Antony Lewis,et al.  Efficient sampling of fast and slow cosmological parameters , 2013, 1304.4473.

[147]  A. Pettitt,et al.  Recursive Pathways to Marginal Likelihood Estimation with Prior-Sensitivity Analysis , 2013, 1301.6450.

[148]  Nicholas G. Polson,et al.  Split Sampling: Expectations, Normalisation and Rare Events , 2012, 1212.0534.

[149]  Ning Xiang,et al.  Nested sampling applied in Bayesian room-acoustics decay analysis. , 2012, The Journal of the Acoustical Society of America.

[150]  Ozgur E. Akman,et al.  Nested sampling for parameter inference in systems biology: application to an exemplar circadian model , 2012, BMC Systems Biology.

[151]  Pat Scott,et al.  Pippi — Painless parsing, post-processing and plotting of posterior and likelihood samples , 2012, The European Physical Journal Plus.

[152]  L. Roszkowski,et al.  The CMSSM Favoring New Territories: The Impact of New LHC Limits and a 125 GeV Higgs , 2012 .

[153]  J. Skilling.  Bayesian computation in big spaces-nested sampling and Galilean Monte Carlo , 2012 .

[154]  Pierre Del Moral,et al.  Sequential Monte Carlo for rare event estimation , 2012, Stat. Comput..

[155]  R. Trotta,et al.  Updated global fits of the cMSSM including the latest LHC SUSY and Higgs searches and XENON100 data , 2011, 1112.4192.

[156]  C. Broeck,et al.  Effect of calibration errors on Bayesian parameter estimation for gravitational wave signals from inspiral binary systems in the advanced detectors era , 2011, 1111.3044.

[157]  David L Wild,et al.  Exploring the energy landscapes of protein folding simulations with Bayesian computation. , 2010, Biophysical journal.

[158]  Andrew Gelman,et al.  Handbook of Markov Chain Monte Carlo , 2011 .

[159]  Radford M. Neal.  MCMC Using Hamiltonian Dynamics , 2011, 1206.1901.

[160]  August E. Evrard,et al.  Cosmological Parameters from Observations of Galaxy Clusters , 2011, 1103.4829.

[161]  P. Berkes,et al.  Improved constraints on cosmological parameters from SNIa data , 2011, 1102.3237.

[162]  Charles R. Keeton,et al.  On statistical uncertainty in nested sampling , 2011, 1102.0996.

[163]  F. Feroz,et al.  Bayesian analysis of weak gravitational lensing and Sunyaev-Zel'dovich data for six galaxy clusters , 2011, 1101.5912.

[164]  F. Feroz,et al.  Challenges of profile likelihood evaluation in multi-dimensional SUSY scans , 2011, 1101.3296.

[165]  R. Trotta,et al.  CONSTRAINTS ON COSMIC-RAY PROPAGATION MODELS FROM A GLOBAL BAYESIAN ANALYSIS , 2010, 1011.0037.

[166]  R. Trotta,et al.  Hunting Down the Best Model of Inflation with Bayesian Evidence , 2010, 1009.4157.

[167]  M. Betancourt.  Nested Sampling with Constrained Hamiltonian Monte Carlo , 2010, 1005.0157.

[168]  Brendon J. Brewer,et al.  Diffusive nested sampling , 2009, Stat. Comput..

[169]  George Casella,et al.  A Short History of Markov Chain Monte Carlo: Subjective Recollections from Incomplete Data , 2008, 0808.2902.

[170]  R. Bass,et al.  Review: P. Billingsley, Convergence of probability measures , 1971 .

[171]  M. Sullivan,et al.  SUPERNOVA CONSTRAINTS AND SYSTEMATIC UNCERTAINTIES FROM THE FIRST THREE YEARS OF THE SUPERNOVA LEGACY SURVEY , 2011, 1104.1443.

[172]  Jonathan R Goodman,et al.  Ensemble samplers with affine invariance , 2010 .

[173]  A. Vecchio,et al.  Bayesian coherent analysis of in-spiral gravitational wave signals with a detector network , 2009, 0911.3820.

[174]  J. Conrad,et al.  likelihood analysis of the constrained MSSM with genetic algorithms , 2010 .

[175]  Gábor Csányi,et al.  Efficient sampling of atomic configurational spaces. , 2009, The journal of physical chemistry. B.

[176]  F. Feroz,et al.  Fitting the Phenomenological MSSM , 2009, 0904.2548.

[177]  C. Robert,et al.  Properties of nested sampling , 2008, 0801.3887.

[178]  J. Skilling.  Nested Sampling’s Convergence , 2009 .

[179]  C. Robert,et al.  Computational methods for Bayesian model choice , 2009, 0907.5123.

[180]  F. Feroz,et al.  Bayesian modelling of clusters of galaxies from multifrequency‐pointed Sunyaev–Zel'dovich observations , 2008, 0811.1199.

[181]  F. Feroz,et al.  MultiNest: an efficient and robust Bayesian inference tool for cosmology and particle physics , 2008, 0809.3437.

[182]  F. Feroz,et al.  The impact of priors and observables on parameter inferences in the constrained MSSM , 2008, 0809.3792.

[183]  David Applebaum,et al.  Probability and Information: An Integrated Approach , 2008 .

[184]  R. Trotta.  Bayes in the sky: Bayesian inference and model selection in cosmology , 2008, 0803.4089.

[185]  A. Vecchio,et al.  Bayesian approach to the follow-up of candidate gravitational wave signals , 2008, 0801.4313.

[186]  John D. Hunter,et al.  Matplotlib: A 2D Graphics Environment , 2007, Computing in Science & Engineering.

[187]  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.

[188]  Y. Itoh,et al.  The Post-Newtonian Approximation for Relativistic Compact Binaries , 2007, Living reviews in relativity.

[189]  M. Hobson,et al.  Efficient Bayesian inference for multimodal problems in cosmology , 2007, astro-ph/0701867.

[190]  J. Skilling,et al.  Discussion of Nested Sampling for Bayesian Computations by John Skilling , 2007 .

[191]  Iain Murray.  Advances in Markov chain Monte Carlo methods , 2007 .

[192]  Tony O’Hagan.  Bayes factors , 2006 .

[193]  J. Skilling.  Nested sampling for general Bayesian computation , 2006 .

[194]  David J Wales,et al.  Potential energy and free energy landscapes. , 2006, The journal of physical chemistry. B.

[195]  Cajo J. F. ter Braak,et al.  A Markov Chain Monte Carlo version of the genetic algorithm Differential Evolution: easy Bayesian computing for real parameter spaces , 2006, Stat. Comput..

[196]  Donald B. Rubin,et al.  Validation of Software for Bayesian Models Using Posterior Quantiles , 2006 .

[197]  D. Parkinson,et al.  Bayesian model selection analysis of WMAP3 , 2006, astro-ph/0605003.

[198]  R. Trotta,et al.  A Markov chain Monte Carlo analysis of the CMSSM , 2006, hep-ph/0602028.

[199]  D. Parkinson,et al.  A Nested Sampling Algorithm for Cosmological Model Selection , 2005, astro-ph/0508461.

[200]  Zoubin Ghahramani,et al.  Nested sampling for Potts models , 2005, NIPS.

[201]  P. Gregory.  Bayesian Logical Data Analysis for the Physical Sciences: Multivariate Gaussian from maximum entropy , 2005 .

[202]  D. Wales.  The energy landscape as a unifying theme in molecular science , 2005, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[203]  George Woodworth,et al.  Bayesian Reasoning in Data Analysis: A Critical Introduction , 2004 .

[204]  David J. C. MacKay,et al.  Information Theory, Inference, and Learning Algorithms , 2004, IEEE Transactions on Information Theory.

[205]  Giulio D'Agostini,et al.  BAYESIAN REASONING IN DATA ANALYSIS: A CRITICAL INTRODUCTION , 2003 .

[206]  Edward J. Wollack,et al.  First-Year Wilkinson Microwave Anisotropy Probe (WMAP) Observations: Determination of Cosmological Parameters , 2003, astro-ph/0302209.

[207]  David J. Wales,et al.  Free energy landscapes of model peptides and proteins , 2003 .

[208]  Sergei V. Krivov,et al.  Free energy disconnectivity graphs: Application to peptide models , 2002 .

[209]  Stefano Giordano,et al.  Rare event simulation , 2002, Eur. Trans. Telecommun..

[210]  A. Lewis,et al.  Cosmological parameters from CMB and other data: A Monte Carlo approach , 2002, astro-ph/0205436.

[211]  S. Allen,et al.  Cosmological constraints from the X-ray gas mass fraction in relaxed lensing clusters observed with Chandra , 2002, astro-ph/0205007.

[212]  J. Beck,et al.  Estimation of Small Failure Probabilities in High Dimensions by Subset Simulation , 2001 .

[213]  D. Landau,et al.  Efficient, multiple-range random walk algorithm to calculate the density of states. , 2000, Physical review letters.

[214]  C. E. SHANNON,et al.  A mathematical theory of communication , 1948, MOCO.

[215]  F. Sciortino,et al.  Thermodynamics of supercooled liquids in the inherent-structure formalism: a case study , 1999, cond-mat/9911062.

[216]  H. Scheraga,et al.  Global optimization of clusters, crystals, and biomolecules. , 1999, Science.

[217]  I. Hook,et al.  Measurements of Ω and Λ from 42 High-Redshift Supernovae , 1998, astro-ph/9812133.

[218]  Mark A. Miller,et al.  Archetypal energy landscapes , 1998, Nature.

[219]  A. Riess,et al.  Observational Evidence from Supernovae for an Accelerating Universe and a Cosmological Constant , 1998, astro-ph/9805201.

[220]  Xiao-Li Meng,et al.  Simulating Normalizing Constants: From Importance Sampling to Bridge Sampling to Path Sampling , 1998 .

[221]  S. P. Martin,et al.  Perceptual Content , 1994 .

[222]  J. Doye,et al.  Global Optimization by Basin-Hopping and the Lowest Energy Structures of Lennard-Jones Clusters Containing up to 110 Atoms , 1997, cond-mat/9803344.

[223]  M. Karplus,et al.  The topology of multidimensional potential energy surfaces: Theory and application to peptide structure and kinetics , 1997 .

[224]  S. Chib.  Marginal Likelihood from the Gibbs Output , 1995 .

[225]  Liddle,et al.  Formalizing the slow-roll approximation in inflation. , 1994, Physical review. D, Particles and fields.

[226]  M. Newton.  Approximate Bayesian-inference With the Weighted Likelihood Bootstrap , 1994 .

[227]  H. Scheraga,et al.  Monte Carlo-minimization approach to the multiple-minima problem in protein folding. , 1987, Proceedings of the National Academy of Sciences of the United States of America.

[228]  L. Tierney,et al.  Accurate Approximations for Posterior Moments and Marginal Densities , 1986 .

[229]  Robert L. Smith,et al.  Efficient Monte Carlo Procedures for Generating Points Uniformly Distributed over Bounded Regions , 1984, Oper. Res..

[230]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[231]  Michael Creutz,et al.  Microcanonical Monte Carlo Simulation , 1983 .

[232]  A. Dawid.  The Well-Calibrated Bayesian , 1982 .

[233]  A. Guth.  Inflationary universe: A possible solution to the horizon and flatness problems , 1981 .

[234]  B. L. Burrows,et al.  A New Approach to Numerical Integration , 1980 .

[235]  T. Kloek,et al.  Bayesian estimates of equation system parameters, An application of integration by Monte Carlo , 1976 .

[236]  W. K. Hastings,et al.  Monte Carlo Sampling Methods Using Markov Chains and Their Applications , 1970 .

[237]  Edwin T. Jaynes,et al.  Prior Probabilities , 1968, Encyclopedia of Machine Learning.

[238]  Ian R. McDonald,et al.  Machine Calculation of Thermodynamic Properties of a Simple Fluid at Supercritical Temperatures , 1967 .

[239]  Richard Bellman,et al.  Adaptive Control Processes: A Guided Tour , 1961, The Mathematical Gazette.

[240]  H. H. Rosenbrock,et al.  An Automatic Method for Finding the Greatest or Least Value of a Function , 1960, Comput. J..

[241]  P. Whittle.  Curve and Periodogram Smoothing , 1957 .

[242]  N. Metropolis,et al.  Equation of State Calculations by Fast Computing Machines , 1953, Resonance.

[243]  CLAUSES COVERING DUTY.  American Institute. , 1892 .

[244]  J. Kirkwood.  Statistical Mechanics of Fluid Mixtures , 1935 .

[245]  Robert L. Smith,et al.  Hit-and-Run Methods , 2022 .