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Hierarchical Dirichlet Processes

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Cited by:

  1. Bauwens, Luc & Dufays, Arnaud & Rombouts, Jeroen V.K., 2014. "Marginal likelihood for Markov-switching and change-point GARCH models," Journal of Econometrics, Elsevier, vol. 178(P3), pages 508-522.
  2. Csereklyei, Zsuzsanna & Anantharama, Nandini & Kallies, Anne, 2021. "Electricity market transitions in Australia: Evidence using model-based clustering," Energy Economics, Elsevier, vol. 103(C).
  3. Hartman, Brian M. & Heaton, Matthew J., 2011. "Accounting for regime and parameter uncertainty in regime-switching models," Insurance: Mathematics and Economics, Elsevier, vol. 49(3), pages 429-437.
  4. Leisen, Fabrizio & Lijoi, Antonio, 2011. "Vectors of two-parameter Poisson-Dirichlet processes," Journal of Multivariate Analysis, Elsevier, vol. 102(3), pages 482-495, March.
  5. Boyuan Zhang, 2022. "Incorporating Prior Knowledge of Latent Group Structure in Panel Data Models," Papers 2211.16714, arXiv.org, revised Oct 2023.
  6. Markus Jochmann, 2015. "Modeling U.S. Inflation Dynamics: A Bayesian Nonparametric Approach," Econometric Reviews, Taylor & Francis Journals, vol. 34(5), pages 537-558, May.
  7. Sergei Seleznev, 2019. "Truncated priors for tempered hierarchical Dirichlet process vector autoregression," Bank of Russia Working Paper Series wps47, Bank of Russia.
  8. Michelle Dietzen & Haoran Zhai & Olivia Lucas & Oriol Pich & Christopher Barrington & Wei-Ting Lu & Sophia Ward & Yanping Guo & Robert E. Hynds & Simone Zaccaria & Charles Swanton & Nicholas McGranaha, 2024. "Replication timing alterations are associated with mutation acquisition during breast and lung cancer evolution," Nature Communications, Nature, vol. 15(1), pages 1-23, December.
  9. Maheu, John M. & Yang, Qiao, 2016. "An infinite hidden Markov model for short-term interest rates," Journal of Empirical Finance, Elsevier, vol. 38(PA), pages 202-220.
  10. Shu-Ping Shi & Yong Song, 2012. "Identifying Speculative Bubbles with an Infinite Hidden Markov Model," Working Paper series 26_12, Rimini Centre for Economic Analysis.
  11. Chyi-Kwei Yau & Alan Porter & Nils Newman & Arho Suominen, 2014. "Clustering scientific documents with topic modeling," Scientometrics, Springer;Akadémiai Kiadó, vol. 100(3), pages 767-786, September.
  12. Xin Jin & John M. Maheu & Qiao Yang, 2019. "Bayesian parametric and semiparametric factor models for large realized covariance matrices," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(5), pages 641-660, August.
  13. Jim E. Griffin & Fabrizio Leisen, 2017. "Compound random measures and their use in Bayesian non-parametrics," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(2), pages 525-545, March.
  14. Lauren Hoskovec & Matthew D. Koslovsky & Kirsten Koehler & Nicholas Good & Jennifer L. Peel & John Volckens & Ander Wilson, 2023. "Infinite hidden Markov models for multiple multivariate time series with missing data," Biometrics, The International Biometric Society, vol. 79(3), pages 2592-2604, September.
  15. Minjung Kyung & Jeff Gill & George Casella, 2011. "Sampling schemes for generalized linear Dirichlet process random effects models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 20(3), pages 259-290, August.
  16. Lu Huang & Xiang Chen & Yi Zhang & Changtian Wang & Xiaoli Cao & Jiarun Liu, 2022. "Identification of topic evolution: network analytics with piecewise linear representation and word embedding," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5353-5383, September.
  17. Tianshuang Qiu & Chuanming Yu & Yunci Zhong & Lu An & Gang Li, 2021. "A scientific citation recommendation model integrating network and text representations," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(11), pages 9199-9221, November.
  18. Georg K Gerber & Robin D Dowell & Tommi S Jaakkola & David K Gifford, 2007. "Automated Discovery of Functional Generality of Human Gene Expression Programs," PLOS Computational Biology, Public Library of Science, vol. 3(8), pages 1-15, August.
  19. Redivo, Edoardo & Nguyen, Hien D. & Gupta, Mayetri, 2020. "Bayesian clustering of skewed and multimodal data using geometric skewed normal distributions," Computational Statistics & Data Analysis, Elsevier, vol. 152(C).
  20. Martin, Gael M. & Frazier, David T. & Maneesoonthorn, Worapree & Loaiza-Maya, Rubén & Huber, Florian & Koop, Gary & Maheu, John & Nibbering, Didier & Panagiotelis, Anastasios, 2024. "Bayesian forecasting in economics and finance: A modern review," International Journal of Forecasting, Elsevier, vol. 40(2), pages 811-839.
  21. Gael M. Martin & David T. Frazier & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2023. "Bayesian Forecasting in the 21st Century: A Modern Review," Monash Econometrics and Business Statistics Working Papers 1/23, Monash University, Department of Econometrics and Business Statistics.
  22. Stefan Helmstetter & Heiko Paulheim, 2021. "Collecting a Large Scale Dataset for Classifying Fake News Tweets Using Weak Supervision," Future Internet, MDPI, vol. 13(5), pages 1-25, April.
  23. Bruno Scarpa & David B. Dunson, 2009. "Bayesian Hierarchical Functional Data Analysis Via Contaminated Informative Priors," Biometrics, The International Biometric Society, vol. 65(3), pages 772-780, September.
  24. J. E. Griffin & M. Kolossiatis & M. F. J. Steel, 2013. "Comparing distributions by using dependent normalized random-measure mixtures," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(3), pages 499-529, June.
  25. Shamsi Zamenjani, Azam, 2021. "Do financial variables help predict the conditional distribution of the market portfolio?," Journal of Empirical Finance, Elsevier, vol. 62(C), pages 327-345.
  26. Martin Reisenbichler & Thomas Reutterer, 2019. "Topic modeling in marketing: recent advances and research opportunities," Journal of Business Economics, Springer, vol. 89(3), pages 327-356, April.
  27. Wang, Jason & Weiss, Robert E., 2022. "Local and global topics in text modeling of web pages nested in web sites," Computational Statistics & Data Analysis, Elsevier, vol. 173(C).
  28. Gengjie Jia & Xue Zhong & Hae Kyung Im & Nathan Schoettler & Milton Pividori & D. Kyle Hogarth & Anne I. Sperling & Steven R. White & Edward T. Naureckas & Christopher S. Lyttle & Chikashi Terao & Yoi, 2022. "Discerning asthma endotypes through comorbidity mapping," Nature Communications, Nature, vol. 13(1), pages 1-19, December.
  29. Gianluca Mastrantonio, 2022. "The modelling of movement of multiple animals that share behavioural features," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(4), pages 932-950, August.
  30. Lu Shaochuan, 2023. "Scalable Bayesian Multiple Changepoint Detection via Auxiliary Uniformisation," International Statistical Review, International Statistical Institute, vol. 91(1), pages 88-113, April.
  31. Lei Yang & Xianyi Wu, 2013. "Estimation of Dirichlet process priors with monotone missing data," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 25(4), pages 787-807, December.
  32. Hassan Akell & Farkhondeh-Alsadat Sajadi & Iraj Kazemi, 2023. "Construction of Jointly Distributed Random Samples Drawn from the Beta Two-Parameter Process," Methodology and Computing in Applied Probability, Springer, vol. 25(3), pages 1-12, September.
  33. Hou, Chenghan, 2017. "Infinite hidden markov switching VARs with application to macroeconomic forecast," International Journal of Forecasting, Elsevier, vol. 33(4), pages 1025-1043.
  34. Bassetti, Federico & Casarin, Roberto & Leisen, Fabrizio, 2014. "Beta-product dependent Pitman–Yor processes for Bayesian inference," Journal of Econometrics, Elsevier, vol. 180(1), pages 49-72.
  35. Evelina Gabasova & John Reid & Lorenz Wernisch, 2017. "Clusternomics: Integrative context-dependent clustering for heterogeneous datasets," PLOS Computational Biology, Public Library of Science, vol. 13(10), pages 1-29, October.
  36. Dario Valcamonico & Piero Baraldi & Francesco Amigoni & Enrico Zio, 2024. "A framework based on Natural Language Processing and Machine Learning for the classification of the severity of road accidents from reports," Journal of Risk and Reliability, , vol. 238(5), pages 957-971, October.
  37. Alfio Ferrara & Silvia Salini, 2012. "Ten challenges in modeling bibliographic data for bibliometric analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 93(3), pages 765-785, December.
  38. Wang, Jiangzhou & Cui, Tingting & Zhu, Wensheng & Wang, Pengfei, 2023. "Covariate-modulated large-scale multiple testing under dependence," Computational Statistics & Data Analysis, Elsevier, vol. 180(C).
  39. Huailan Liu & Zhiwang Chen & Jie Tang & Yuan Zhou & Sheng Liu, 2020. "Mapping the technology evolution path: a novel model for dynamic topic detection and tracking," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 2043-2090, December.
  40. Jin, Xin & Maheu, John M. & Yang, Qiao, 2022. "Infinite Markov pooling of predictive distributions," Journal of Econometrics, Elsevier, vol. 228(2), pages 302-321.
  41. Saldaña-Zepeda, Dayna P. & Velasco-Cruz, Ciro & Torres-Preciado, Víctor H., 2020. "Mexican peso-USD exchange rate: A switching linear dynamical model application," International Economics, Elsevier, vol. 162(C), pages 80-91.
  42. Yoshi Fujiwara & Rubaiyat Islam, 2021. "Bitcoin's Crypto Flow Network," Papers 2106.11446, arXiv.org, revised Jul 2021.
  43. Jin, Xin & Maheu, John M., 2016. "Bayesian semiparametric modeling of realized covariance matrices," Journal of Econometrics, Elsevier, vol. 192(1), pages 19-39.
  44. DESCHAMPS, Philippe J., 2016. "Bayesian Semiparametric Forecasts of Real Interest Rate Data," LIDAM Discussion Papers CORE 2016050, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  45. Cai, Bo & Meyer, Renate, 2011. "Bayesian semiparametric modeling of survival data based on mixtures of B-spline distributions," Computational Statistics & Data Analysis, Elsevier, vol. 55(3), pages 1260-1272, March.
  46. Jiang, Hanchen & Qiang, Maoshan & Lin, Peng, 2016. "A topic modeling based bibliometric exploration of hydropower research," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 226-237.
  47. Hongxia Yang & Aurelie Lozano, 2015. "Multi-relational learning via hierarchical nonparametric Bayesian collective matrix factorization," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(5), pages 1133-1147, May.
  48. Jääskinen Väinö & Parkkinen Ville & Cheng Lu & Corander Jukka, 2014. "Bayesian clustering of DNA sequences using Markov chains and a stochastic partition model," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 13(1), pages 105-121, February.
  49. Ji Yeon Lee & Richa Kumari & Jae Yun Jeong & Tae-Hyun Kim & Byeong-Hee Lee, 2020. "Knowledge Discovering on Graphene Green Technology by Text Mining in National R&D Projects in South Korea," Sustainability, MDPI, vol. 12(23), pages 1-16, November.
  50. Peter Müeller & Fernando A. Quintana & Garritt Page, 2018. "Nonparametric Bayesian inference in applications," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 27(2), pages 175-206, June.
  51. Zheng, Jing & Yu, Dongjie & Zhu, Bin & Tong, Changqing, 2022. "Learning hidden Markov models with unknown number of states," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 594(C).
  52. Marta Catalano & Claudio Del Sole & Antonio Lijoi & Igor Prünster, 2024. "A Unified Approach to Hierarchical Random Measures," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 86(1), pages 255-287, November.
  53. Antonio Lijoi & Bernardo Nipoti, 2013. "A class of hazard rate mixtures for combining survival data from different experiments," DEM Working Papers Series 059, University of Pavia, Department of Economics and Management.
  54. Mena, Ramsés H. & Walker, Stephen G., 2012. "An EPPF from independent sequences of geometric random variables," Statistics & Probability Letters, Elsevier, vol. 82(6), pages 1059-1066.
  55. Li Dai & Dahai You & Xianggen Yin, 2020. "Data Driven Robust Energy and Reserve Dispatch Based on a Nonparametric Dirichlet Process Gaussian Mixture Model," Energies, MDPI, vol. 13(18), pages 1-18, September.
  56. Thomas R. W. Oliver & Lia Chappell & Rashesh Sanghvi & Lauren Deighton & Naser Ansari-Pour & Stefan C. Dentro & Matthew D. Young & Tim H. H. Coorens & Hyunchul Jung & Tim Butler & Matthew D. C. Nevill, 2022. "Clonal diversification and histogenesis of malignant germ cell tumours," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
  57. Wang, Xia & Shojaie, Ali & Zou, Jian, 2019. "Bayesian hidden Markov models for dependent large-scale multiple testing," Computational Statistics & Data Analysis, Elsevier, vol. 136(C), pages 123-136.
  58. Yang, Qiao, 2019. "Stock returns and real growth: A Bayesian nonparametric approach," Journal of Empirical Finance, Elsevier, vol. 53(C), pages 53-69.
  59. Goto, Satoshi & Takagishi, Mariko & Yadohisa, Hiroshi, 2021. "Clustering for time-varying relational count data," Computational Statistics & Data Analysis, Elsevier, vol. 156(C).
  60. Rodrigues, G.S. & Nott, David J. & Sisson, S.A., 2016. "Functional regression approximate Bayesian computation for Gaussian process density estimation," Computational Statistics & Data Analysis, Elsevier, vol. 103(C), pages 229-241.
  61. Sudhir Voleti & Praveen K. Kopalle & Pulak Ghosh, 2015. "An Interproduct Competition Model Incorporating Branding Hierarchy and Product Similarities Using Store-Level Data," Management Science, INFORMS, vol. 61(11), pages 2720-2738, November.
  62. Arnaud Dufays & Zhuo Li & Jeroen V.K. Rombouts & Yong Song, 2021. "Sparse change‐point VAR models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(6), pages 703-727, September.
  63. Gustaf Bellstam & Sanjai Bhagat & J. Anthony Cookson, 2021. "A Text-Based Analysis of Corporate Innovation," Management Science, INFORMS, vol. 67(7), pages 4004-4031, July.
  64. J. Griffin, 2011. "Bayesian clustering of distributions in stochastic frontier analysis," Journal of Productivity Analysis, Springer, vol. 36(3), pages 275-283, December.
  65. Yong Song & Tomasz Wo'zniak, 2020. "Markov Switching," Papers 2002.03598, arXiv.org.
  66. Lucas Lehnert & Michael L Littman & Michael J Frank, 2020. "Reward-predictive representations generalize across tasks in reinforcement learning," PLOS Computational Biology, Public Library of Science, vol. 16(10), pages 1-27, October.
  67. Li, Xin & Xie, Qianqian & Daim, Tugrul & Huang, Lucheng, 2019. "Forecasting technology trends using text mining of the gaps between science and technology: The case of perovskite solar cell technology," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 432-449.
  68. Azzimonti, Laura & Corani, Giorgio & Zaffalon, Marco, 2019. "Hierarchical estimation of parameters in Bayesian networks," Computational Statistics & Data Analysis, Elsevier, vol. 137(C), pages 67-91.
  69. Luo, Jiawen & Klein, Tony & Ji, Qiang & Hou, Chenghan, 2022. "Forecasting realized volatility of agricultural commodity futures with infinite Hidden Markov HAR models," International Journal of Forecasting, Elsevier, vol. 38(1), pages 51-73.
  70. Liu Yang & Nandram Balgobin, 2022. "Sampling methods for the concentration parameter and discrete baseline of the Dirichlet Process," Statistics in Transition New Series, Statistics Poland, vol. 23(4), pages 21-36, December.
  71. Koltcov, Sergei, 2018. "Application of Rényi and Tsallis entropies to topic modeling optimization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 1192-1204.
  72. Crook Oliver M. & Gatto Laurent & Kirk Paul D. W., 2019. "Fast approximate inference for variable selection in Dirichlet process mixtures, with an application to pan-cancer proteomics," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 18(6), pages 1-20, December.
  73. Sonia Petrone & Michele Guindani & Alan E. Gelfand, 2009. "Hybrid Dirichlet mixture models for functional data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(4), pages 755-782, September.
  74. Antonio Lijoi & Igor Pruenster & Stephen G. Walker, 2008. "Bayesian nonparametric estimators derived from conditional Gibbs structures," ICER Working Papers - Applied Mathematics Series 06-2008, ICER - International Centre for Economic Research.
  75. Bassetti, Federico & Casarin, Roberto & Leisen, Fabrizio, 2011. "Beta-product Poisson-Dirichlet Processes," DES - Working Papers. Statistics and Econometrics. WS 12160, Universidad Carlos III de Madrid. Departamento de Estadística.
  76. Antonio Lijoi & Igor Prünster & Giovanni Rebaudo, 2023. "Flexible clustering via hidden hierarchical Dirichlet priors," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 50(1), pages 213-234, March.
  77. Jiapeng Liu & Miłosz Kadziński & Xiuwu Liao, 2023. "Modeling Contingent Decision Behavior: A Bayesian Nonparametric Preference-Learning Approach," INFORMS Journal on Computing, INFORMS, vol. 35(4), pages 764-785, July.
  78. Kiranmoy Das & Bhuvanesh Pareek & Sarah Brown & Pulak Ghosh, 2017. "A Semiparametric Bayesian Approach to a New Dynamic Zero-Inflated Model," Working Papers 2017001, The University of Sheffield, Department of Economics.
  79. Bernard C. H. Lee & Philip S. Robinson & Tim H. H. Coorens & Helen H. N. Yan & Sigurgeir Olafsson & Henry Lee-Six & Mathijs A. Sanders & Hoi Cheong Siu & James Hewinson & Sarah S. K. Yue & Wai Yin Tsu, 2022. "Mutational landscape of normal epithelial cells in Lynch Syndrome patients," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
  80. Fortini, S. & Petrone, S., 2012. "Hierarchical reinforced urn processes," Statistics & Probability Letters, Elsevier, vol. 82(8), pages 1521-1529.
  81. Michele Guindani & Wesley O. Johnson, 2018. "More nonparametric Bayesian inference in applications," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 27(2), pages 239-251, June.
  82. Parvin Ahmadi & Iman Gholampour & Mahmoud Tabandeh, 2018. "Cluster-based sparse topical coding for topic mining and document clustering," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 12(3), pages 537-558, September.
  83. XuanLong Nguyen & Alan Gelfand, 2014. "Bayesian nonparametric modeling for functional analysis of variance," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 66(3), pages 495-526, June.
  84. Yordan P Raykov & Alexis Boukouvalas & Fahd Baig & Max A Little, 2016. "What to Do When K-Means Clustering Fails: A Simple yet Principled Alternative Algorithm," PLOS ONE, Public Library of Science, vol. 11(9), pages 1-28, September.
  85. CARPANTIER, Jean-François & DUFAYS, Arnaud, 2014. "Specific Markov-switching behaviour for ARMA parameters," LIDAM Discussion Papers CORE 2014014, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  86. Liu, Hefei & Song, Xinyuan, 2021. "Bayesian analysis of hidden Markov structural equation models with an unknown number of hidden states," Econometrics and Statistics, Elsevier, vol. 18(C), pages 29-43.
  87. C. Yau & O. Papaspiliopoulos & G. O. Roberts & C. Holmes, 2011. "Bayesian non‐parametric hidden Markov models with applications in genomics," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(1), pages 37-57, January.
  88. Michael L. Pennell & David B. Dunson, 2008. "Nonparametric Bayes Testing of Changes in a Response Distribution with an Ordinal Predictor," Biometrics, The International Biometric Society, vol. 64(2), pages 413-423, June.
  89. Urbi Garay & Enrique Ter Horst & German Molina & Abel Rodriguez, 2016. "Bayesian Nonparametric Measurement of Factor Betas and Clustering with Application to Hedge Fund Returns," Econometrics, MDPI, vol. 4(1), pages 1-23, March.
  90. Jia Liu & John M. Maheu, 2018. "Improving Markov switching models using realized variance," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(3), pages 297-318, April.
  91. Camerlenghi, Federico & Lijoi, Antonio & Prünster, Igor, 2017. "Bayesian prediction with multiple-samples information," Journal of Multivariate Analysis, Elsevier, vol. 156(C), pages 18-28.
  92. Arunabha Majumdar & Sourabh Bhattacharya & Analabha Basu & Saurabh Ghosh, 2013. "A Novel Bayesian Semiparametric Algorithm for Inferring Population Structure and Adjusting for Case-Control Association Tests," Biometrics, The International Biometric Society, vol. 69(1), pages 164-173, March.
  93. Occhini, Giulia & Tranos, Emmanouil & Wolf, Levi John, 2023. "Occupational segregation in the digital economy? A Natural Language Processing approach using UK Web Data," SocArXiv z8xta, Center for Open Science.
  94. Wenkai Zhou & Chi Zhang & Linwan Wu & Meghana Shashidhar, 2023. "ChatGPT and marketing: Analyzing public discourse in early Twitter posts," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(4), pages 693-706, December.
  95. Robert M. Dorazio & Bhramar Mukherjee & Li Zhang & Malay Ghosh & Howard L. Jelks & Frank Jordan, 2008. "Modeling Unobserved Sources of Heterogeneity in Animal Abundance Using a Dirichlet Process Prior," Biometrics, The International Biometric Society, vol. 64(2), pages 635-644, June.
  96. Li, Xin & Wen, Yang & Jiang, Jiaojiao & Daim, Tugrul & Huang, Lucheng, 2022. "Identifying potential breakthrough research: A machine learning method using scientific papers and Twitter data," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
  97. Hanchen Jiang & Maoshan Qiang & Dongcheng Zhang & Qi Wen & Bingqing Xia & Nan An, 2018. "Climate Change Communication in an Online Q&A Community: A Case Study of Quora," Sustainability, MDPI, vol. 10(5), pages 1-17, May.
  98. Li, Chenxing, 2022. "A multivariate GARCH model with an infinite hidden Markov mixture," MPRA Paper 112792, University Library of Munich, Germany.
  99. Sylvie Tchumtchoua & Dipak Dey, 2012. "Modeling Associations Among Multivariate Longitudinal Categorical Variables in Survey Data: A Semiparametric Bayesian Approach," Psychometrika, Springer;The Psychometric Society, vol. 77(4), pages 670-692, October.
  100. Qiao, Xinghao & Liu, Yirui & Lam, Jessica, 2022. "CATVI: conditional and adaptively truncated variational inference for hierarchical Bayesian nonparametric models," LSE Research Online Documents on Economics 114639, London School of Economics and Political Science, LSE Library.
  101. Daniel Ting & Guoli Wang & Maxim Shapovalov & Rajib Mitra & Michael I Jordan & Roland L Dunbrack Jr, 2010. "Neighbor-Dependent Ramachandran Probability Distributions of Amino Acids Developed from a Hierarchical Dirichlet Process Model," PLOS Computational Biology, Public Library of Science, vol. 6(4), pages 1-21, April.
  102. Martínez-Ovando Juan Carlos & Walker Stephen G., 2011. "Time-series Modelling, Stationarity and Bayesian Nonparametric Methods," Working Papers 2011-08, Banco de México.
  103. Yong Song, 2014. "Modelling Regime Switching And Structural Breaks With An Infinite Hidden Markov Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(5), pages 825-842, August.
  104. Fabrizio Leisen & Antonio Lijoi, 2010. "Vectors of two-parameter Poisson-Dirichlet processes," Quaderni di Dipartimento 119, University of Pavia, Department of Economics and Quantitative Methods.
  105. Simon Fritzsch & Philipp Scharner & Gregor Weiß, 2021. "Estimating the relation between digitalization and the market value of insurers," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 88(3), pages 529-567, September.
  106. Jeffrey L. Furman & Florenta Teodoridis, 2020. "Automation, Research Technology, and Researchers’ Trajectories: Evidence from Computer Science and Electrical Engineering," Organization Science, INFORMS, vol. 31(2), pages 330-354, March.
  107. Jim Griffin & Maria Kalli & Mark Steel, 2018. "Discussion of “Nonparametric Bayesian Inference in Applications”: Bayesian nonparametric methods in econometrics," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 27(2), pages 207-218, June.
  108. Chenxing Li & John M. Maheu & Qiao Yang, 2024. "An infinite hidden Markov model with stochastic volatility," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(6), pages 2187-2211, September.
  109. Philip S. Robinson & Laura E. Thomas & Federico Abascal & Hyunchul Jung & Luke M. R. Harvey & Hannah D. West & Sigurgeir Olafsson & Bernard C. H. Lee & Tim H. H. Coorens & Henry Lee-Six & Laura Butlin, 2022. "Inherited MUTYH mutations cause elevated somatic mutation rates and distinctive mutational signatures in normal human cells," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
  110. Bogojeska Jasmina & Lengauer Thomas, 2012. "Hierarchical Bayes Model for Predicting Effectiveness of HIV Combination Therapies," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(3), pages 1-21, April.
  111. Matthew Gentzkow & Bryan T. Kelly & Matt Taddy, 2017. "Text as Data," NBER Working Papers 23276, National Bureau of Economic Research, Inc.
  112. Andrew Cron & Cécile Gouttefangeas & Jacob Frelinger & Lin Lin & Satwinder K Singh & Cedrik M Britten & Marij J P Welters & Sjoerd H van der Burg & Mike West & Cliburn Chan, 2013. "Hierarchical Modeling for Rare Event Detection and Cell Subset Alignment across Flow Cytometry Samples," PLOS Computational Biology, Public Library of Science, vol. 9(7), pages 1-14, July.
  113. Gianluca Mastrantonio & Giovanna Jona Lasinio & Alessio Pollice & Lorenzo Teodonio & Giulia Capotorti, 2022. "A Dirichlet process model for change‐point detection with multivariate bioclimatic data," Environmetrics, John Wiley & Sons, Ltd., vol. 33(1), February.
  114. Siyuan Liu & Shaojie Tang & Jiangchuan Zheng & Lionel M. Ni, 2022. "Unsupervised Learning for Human Mobility Behaviors," INFORMS Journal on Computing, INFORMS, vol. 34(3), pages 1565-1586, May.
  115. Liu, Yirui & Qiao, Xinghao & Pei, Yulong & Wang, Liying, 2024. "Deep functional factor models: forecasting high-dimensional functional time series via Bayesian nonparametric factorization," LSE Research Online Documents on Economics 125587, London School of Economics and Political Science, LSE Library.
  116. Ying Liao & Yisha Xiang & Min Wang, 2021. "Health assessment and prognostics based on higher‐order hidden semi‐Markov models," Naval Research Logistics (NRL), John Wiley & Sons, vol. 68(2), pages 259-276, March.
  117. Antonio Lijoi & Bernardo Nipoti, 2014. "A Class of Hazard Rate Mixtures for Combining Survival Data From Different Experiments," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(506), pages 802-814, June.
  118. Creighton Heaukulani & Abhinav Pandey & Lancelot F. James, 2024. "Modelling financial volume curves with hierarchical Poisson processes," Papers 2406.19402, arXiv.org.
  119. Jeong Hwan Kook & Michele Guindani & Linlin Zhang & Marina Vannucci, 2019. "NPBayes-fMRI: Non-parametric Bayesian General Linear Models for Single- and Multi-Subject fMRI Data," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 11(1), pages 3-21, April.
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