{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T03:09:20Z","timestamp":1791515360310,"version":"4.3.4"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783319461274","type":"print"},{"value":"9783319461281","type":"electronic"}],"license":[{"start":{"date-parts":[[2016,1,1]],"date-time":"2016-01-01T00:00:00Z","timestamp":1451606400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2016,1,1]],"date-time":"2016-01-01T00:00:00Z","timestamp":1451606400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016]]},"DOI":"10.1007\/978-3-319-46128-1_7","type":"book-chapter","created":{"date-parts":[[2016,9,3]],"date-time":"2016-09-03T05:34:23Z","timestamp":1472880863000},"page":"98-114","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Laplacian Hamiltonian Monte Carlo"],"prefix":"10.1007","author":[{"given":"Yizhe","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Changyou","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ricardo","family":"Henao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lawrence","family":"Carin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2016,9,4]]},"reference":[{"key":"7_CR1","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1063\/1.3037135","volume":"18","author":"C Andrieu","year":"2008","unstructured":"Andrieu, C., Thoms, J.: A tutorial on adaptive mcmc. Stat. Comput. 18, 4 (2008)","journal-title":"Stat. Comput."},{"key":"7_CR2","unstructured":"Bache, K., Lichman, M.: UCI machine learning repository (2013)"},{"key":"7_CR3","unstructured":"Betancourt, M., Byrne, S., Girolami, M.: Optimizing the integrator step size for Hamiltonian Monte Carlo. ArXiv (2014)"},{"key":"7_CR4","unstructured":"Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3 (2003)"},{"key":"7_CR5","doi-asserted-by":"crossref","unstructured":"Brooks, S., Gelman, A., Jones, G., Meng, X.-L.: Handbook of Markov Chain Monte Carlo. CRC Press, Boca Raton (2011)","DOI":"10.1201\/b10905"},{"key":"7_CR6","unstructured":"Chao, W.-L., Solomon, J., Michels, D., Sha, F.: Exponential integration for Hamiltonian Monte Carlo. In: ICML (2015)"},{"key":"7_CR7","unstructured":"Chen, C., Rao, V., Buntine, W., Whye Teh, Y.: Dependent normalized random measures. In: ICML (2013)"},{"key":"7_CR8","unstructured":"Ding, N., Fang, Y., Babbush, R., Chen, C., Skeel, R.D., Neven, H.: Bayesian sampling using stochastic gradient thermostats. In: NIPS (2014)"},{"key":"7_CR9","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1016\/0370-2693(87)91197-X","volume":"195","author":"S Duane","year":"1987","unstructured":"Duane, S., Kennedy, A.D., Pendleton, B.J., Roweth, D.: Hybrid Monte Carlo. Phys. Lett. B 195, 2 (1987)","journal-title":"Phys. Lett. B"},{"issue":"2","key":"7_CR10","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1111\/j.1467-9868.2010.00765.x","volume":"73","author":"Mark Girolami","year":"2011","unstructured":"Girolami, M., Calderhead, B.: Riemann manifold Langevin and Hamiltonian Monte Carlo methods. J. Roy. Stat. Soc. Ser. B (Stat. Method.) 73, 2 (2011)","journal-title":"Journal of the Royal Statistical Society: Series B (Statistical Methodology)"},{"key":"7_CR11","unstructured":"Goldstein, H.: Classical Mechanics. Pearson Education India, New Delhi (1965)"},{"key":"7_CR12","doi-asserted-by":"crossref","unstructured":"Haario, H., Saksman, E., Tamminen, J.: An adaptive Metropolis algorithm. Bernoulli (2001)","DOI":"10.2307\/3318737"},{"key":"7_CR13","first-page":"1","volume":"15","author":"MD Homan","year":"2014","unstructured":"Homan, M.D., Gelman, A.: The no-u-turn sampler: adaptively setting path lengths in hamiltonian monte carlo. J. Mach. Learn. Res. 15, 1 (2014)","journal-title":"J. Mach. Learn. Res."},{"key":"7_CR14","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1007\/s11222-005-4069-4","volume":"15","author":"D Karlis","year":"2005","unstructured":"Karlis, D., Meligkotsidou, L.: Multivariate poisson regression with covariance structure. Stat. Comput. 15, 4 (2005)","journal-title":"Stat. Comput."},{"key":"7_CR15","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1063\/1.1699114","volume":"21","author":"N Metropolis","year":"1953","unstructured":"Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N., Teller, A.H., Teller, E.: Equation of state calculations by fast computing machines. J. Chem. Phys. 21, 6 (1953)","journal-title":"J. Chem. Phys."},{"key":"7_CR16","unstructured":"Mohamed, S., De Freitas, N., et al.: Adaptive hamiltonian and riemann manifold monte carlo samplers. Arxiv (2013)"},{"key":"7_CR17","unstructured":"Murray, I., Adams, R.P., MacKay, D.J.: Elliptical slice sampling. ArXiv (2009)"},{"key":"7_CR18","unstructured":"Neal, R.M.: Probabilistic inference using markov chain monte carlo methods. Technical report CRG-TR-93-1 (1993)"},{"issue":"3","key":"7_CR19","first-page":"705","volume":"31","author":"Radford M. Neal","year":"2003","unstructured":"Neal, R.M.: Slice sampling. Ann. Stat. 31, 705\u2013767 (2003)","journal-title":"The Annals of Statistics"},{"key":"7_CR20","doi-asserted-by":"crossref","unstructured":"Neal, R.M.: MCMC using Hamiltonian dynamics. Handbook of Markov Chain Monte Carlo 2 (2011)","DOI":"10.1201\/b10905-6"},{"key":"7_CR21","unstructured":"Pakman, A., Paninski, L.: Auxiliary-variable exact Hamiltonian Monte Carlo samplers for binary distributions. In: NIPS (2013)"},{"key":"7_CR22","doi-asserted-by":"crossref","unstructured":"Robert, C., Casella, G.: Monte Carlo statistical methods. Springer Science & Business Media, New York (2004)","DOI":"10.1007\/978-1-4757-4145-2"},{"key":"7_CR23","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1198\/jcgs.2009.06134","volume":"18","author":"GO Roberts","year":"2009","unstructured":"Roberts, G.O., Rosenthal, J.S.: Examples of adaptive MCMC. J. Comput. Graph. Stat. 18, 2 (2009)","journal-title":"J. Comput. Graph. Stat."},{"key":"7_CR24","unstructured":"Sohl-Dickstein, J., Mudigonda, M., DeWeese, M.R.: Hamiltonian monte carlo without detailed balance. ArXiv (2014)"},{"key":"7_CR25","unstructured":"Taylor, J.R.: Classical Mechanics. University Science Books, Colorado (2005)"},{"key":"7_CR26","unstructured":"Zhang, Y., Ghahramani, Z., Storkey, A.J., Sutton, C.A.: Continuous relaxations for discrete Hamiltonian Monte Carlo. In: NIPS (2012)"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-46128-1_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,5]],"date-time":"2021-09-05T00:10:48Z","timestamp":1630800648000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-319-46128-1_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016]]},"ISBN":["9783319461274","9783319461281"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-46128-1_7","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016]]},"assertion":[{"value":"4 September 2016","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Riva del Garda","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2016","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2016","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2016","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2016","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}