{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T17:20:00Z","timestamp":1740158400622,"version":"3.37.3"},"reference-count":26,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2019,4,2]],"date-time":"2019-04-02T00:00:00Z","timestamp":1554163200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["K\u00fcnstl Intell"],"published-print":{"date-parts":[[2019,12]]},"DOI":"10.1007\/s13218-019-00580-7","type":"journal-article","created":{"date-parts":[[2019,4,2]],"date-time":"2019-04-02T06:40:23Z","timestamp":1554187223000},"page":"389-399","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["On the Applicability of Probabilistic Programming Languages for Causal Activity Recognition"],"prefix":"10.1007","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1488-4236","authenticated-orcid":false,"given":"Stefan","family":"L\u00fcdtke","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maximilian","family":"Popko","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thomas","family":"Kirste","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,4,2]]},"reference":[{"issue":"3","key":"580_CR1","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1111\/j.1467-9868.2009.00736.x","volume":"72","author":"C Andrieu","year":"2010","unstructured":"Andrieu C, Doucet A, Holenstein R (2010) Particle markov chain monte carlo methods. J R Stat Soc Ser B (Stat Methodol) 72(3):269\u2013342","journal-title":"J R Stat Soc Ser B (Stat Methodol)"},{"key":"580_CR2","unstructured":"Bingham E, Chen J.P, Jankowiak M, Obermeyer F, Pradhan N, Karaletsos T, Singh R, Szerlip P, Horsfall P, Goodman N.D (2018) Pyro: Deep Universal Probabilistic Programming. arXiv preprint \narXiv:1810.09538"},{"key":"580_CR3","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1007\/978-3-642-19718-5_5","volume-title":"Programming Languages and Systems","author":"Johannes Borgstr\u00f6m","year":"2011","unstructured":"Borgstr\u00f6m J, Gordon A.D, Greenberg M, Margetson J, Gael J.V (2011) Measure transformer semantics for Bayesian machine learning. In: European symposium on programming, pp. 77\u201396. Springer, Berlin. \nhttps:\/\/doi.org\/10.1007\/978-3-642-19718-5_5"},{"key":"580_CR4","doi-asserted-by":"publisher","unstructured":"Carpenter B, Gelman A, Hoffman M.D, Lee D, Goodrich B, Betancourt M, Brubaker M, Guo J, Li P, Riddell A (2017) Stan: a probabilistic programming language. J Stat Softw. \nhttps:\/\/doi.org\/10.18637\/jss.v076.i01","DOI":"10.18637\/jss.v076.i01"},{"key":"580_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.2200\/S00692ED1V01Y201601AIM032","volume":"10","author":"L Raedt De","year":"2016","unstructured":"De Raedt L, Kersting K, Natarajan S, Poole D (2016) Statistical relational artificial intelligence: logic, probability, and computation. Synth Lect Artif Intell Mach Learn 10:1\u2013189","journal-title":"Synth Lect Artif Intell Mach Learn"},{"key":"580_CR6","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-3437-9","volume-title":"Sequential Monte Carlo Methods in Practice","author":"A Doucet","year":"2001","unstructured":"Doucet A, de Freitas N, Gordon N (2001) Sequential Monte Carlo Methods in Practice. Springer, Berlin"},{"issue":"3","key":"580_CR7","doi-asserted-by":"publisher","first-page":"358","DOI":"10.1017\/S1471068414000076","volume":"15","author":"D Fierens","year":"2015","unstructured":"Fierens D, Van den Broeck G, Renkens J, Shterionov D, Gutmann B, Thon I, Janssens G, De Raedt L (2015) Inference and learning in probabilistic logic programs using weighted boolean formulas. Theory Pract Logic Programm 15(3):358\u2013401","journal-title":"Theory Pract Logic Programm"},{"key":"580_CR8","unstructured":"Goodman N, Mansinghka V, Roy D, Bonawitz K, Tenenbaum J (2008) Church: a language for generative models. In: Proceedings of the conference on uncertainty in artificial intelligence"},{"key":"580_CR9","unstructured":"Goodman N.D, Stuhlm\u00fcller A (2014) The design and implementation of probabilistic programming languages. \nhttp:\/\/dippl.org\n\n. Accessed 29 Mar 2018"},{"key":"580_CR10","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511613586","volume-title":"Finite Markov chains and algorithmic applications","author":"O H\u00e4ggstr\u00f6m","year":"2002","unstructured":"H\u00e4ggstr\u00f6m O (2002) Finite Markov chains and algorithmic applications, vol 52. Cambridge University Press, Cambridge"},{"issue":"11","key":"580_CR11","doi-asserted-by":"publisher","first-page":"e109381","DOI":"10.1371\/journal.pone.0109381","volume":"9","author":"F Kr\u00fcger","year":"2014","unstructured":"Kr\u00fcger F, Nyolt M, Yordanova K, Hein A, Kirste T (2014) Computational state space models for activity and intention recognition. A feasibility study. PLoS One 9(11):e109381. \nhttps:\/\/doi.org\/10.1371\/journal.pone.0109381","journal-title":"PLoS One"},{"key":"580_CR12","doi-asserted-by":"publisher","unstructured":"Kr\u00fcger F, Steiniger A, Bader S, Kirste T (2012) Evaluating the robustness of activity recognition using computational causal behavior models. In: Proceedings of the international workshop on situation, activity and goal awareness held at Ubicomp 2012, pp. 1066\u20131074. ACM, Pittsburgh, PA, USA. \nhttps:\/\/doi.org\/10.1145\/2370216.2370443","DOI":"10.1145\/2370216.2370443"},{"key":"580_CR13","unstructured":"Kulkarni T.D, Kohli P, Tenenbaum J.B, Mansinghka V (2015) Picture: a probabilistic programming language for scene perception. In: The IEEE conference on computer vision and pattern recognition (CVPR), pp. 4390\u20134399"},{"issue":"4","key":"580_CR14","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1023\/A:1008929526011","volume":"10","author":"DJ Lunn","year":"2000","unstructured":"Lunn DJ, Thomas A, Best N, Spiegelhalter D (2000) Winbugs\u2014a bayesian modelling framework: concepts, structure, and extensibility. Stat Comput 10(4):325\u2013337","journal-title":"Stat Comput"},{"key":"580_CR15","unstructured":"McCallum A, Schultz K, Singh S (2009) FACTORIE: probabilistic programming via imperatively defined factor graphs. In: Bengio Y, Schuurmans D, Lafferty JD, Williams CKI, Culotta A (eds) Advances in neural information processing systems 22, Curran Associates, Inc., pp. 1249\u20131257. \nhttp:\/\/papers.nips.cc\/paper\/3654-factorie-probabilistic-programming-via-imperatively-defined-factor-graphs.pdf"},{"key":"580_CR16","doi-asserted-by":"publisher","unstructured":"Nyolt M, Kirste T (2015) On resampling for Bayesian filters in discrete state spaces. In: Proceedings 2015 IEEE 27th international conference on tools with artificial intelligence, pp. 526\u2013533. IEEE computer society. \nhttps:\/\/doi.org\/10.1109\/ICTAI.2015.83","DOI":"10.1109\/ICTAI.2015.83"},{"key":"580_CR17","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.ijar.2015.04.003","volume":"61","author":"Martin Nyolt","year":"2015","unstructured":"Nyolt M, Kr\u00fcger F, Yordanova K, Hein A, Kirste T (2015-06) Marginal filtering in large state spaces. Int J Approx Reason 61:16\u201332. \nhttps:\/\/doi.org\/10.1016\/j.ijar.2015.04.003","journal-title":"International Journal of Approximate Reasoning"},{"key":"580_CR18","unstructured":"Paige B, Wood F (2014) A compilation target for probabilistic programming languages. In: Xing EP, Jebara T (eds) Proceedings of the 31st international conference on machine learning, proceedings of machine learning research, vol 32, pp. 1935\u20131943. PMLR, Beijing, China"},{"issue":"4","key":"580_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v035.i04","volume":"35","author":"A Patil","year":"2010","unstructured":"Patil A, Huard D, Fonnesbeck CJ (2010) Pymc: Bayesian stochastic modelling in python. J Stat Softw 35(4):1","journal-title":"J Stat Softw"},{"key":"580_CR20","volume-title":"Practical probabilistic programming","author":"A Pfeffer","year":"2016","unstructured":"Pfeffer A (2016) Practical probabilistic programming, 1st edn. Manning Publications Co., Shelter Island","edition":"1"},{"key":"580_CR21","first-page":"458","volume":"6","author":"H Poon","year":"2006","unstructured":"Poon H, Domingos P (2006) Sound and efficient inference with probabilistic and deterministic dependencies. AAAI 6:458\u2013463","journal-title":"AAAI"},{"key":"580_CR22","doi-asserted-by":"publisher","unstructured":"Popko M, L\u00fcdtke S (2018) On the applicability of probabilistic programming languages for causal activity recognition. \nhttps:\/\/doi.org\/10.5281\/zenodo.1635591","DOI":"10.5281\/zenodo.1635591"},{"key":"580_CR23","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1007\/978-3-540-78652-8_5","volume-title":"Probabilistic Inductive Logic Programming","author":"Taisuke Sato","year":"2008","unstructured":"Sato T, Kameya Y (2008) New advances in logic-based probabilistic modeling by PRISM. Springer, Berlin. pp 118\u2013155 \nhttps:\/\/doi.org\/10.1007\/978-3-540-78652-8_5"},{"key":"580_CR24","doi-asserted-by":"crossref","unstructured":"Tolpin D, van de Meert JW, Yang H, Wood F (2016) Design and implementation of probabilistic programming language Anglican. In: Proceedings of the 28th symposium on the implementation and application of functional programming languages, pp. 6:1\u20136:12. ACM","DOI":"10.1145\/3064899.3064910"},{"key":"580_CR25","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1016\/j.ijar.2016.07.001","volume":"78","author":"Calin Rares Turliuc","year":"2016","unstructured":"Turliuc CR, Dickens L, Russo A, Broda K (2016-11) Probabilistic abductive logic programming using Dirichlet priors. Int J Approx Reason 78:223\u2013240. \nhttps:\/\/doi.org\/10.1016\/j.ijar.2016.07.001","journal-title":"International Journal of Approximate Reasoning"},{"key":"580_CR26","unstructured":"Wood F, van de Meert JW, Mansinghka V (2014) A new approach to probabilistic programming inference. In: Artificial intelligence and statistics"}],"container-title":["KI - K\u00fcnstliche Intelligenz"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s13218-019-00580-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s13218-019-00580-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s13218-019-00580-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,3,31]],"date-time":"2020-03-31T23:33:02Z","timestamp":1585697582000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s13218-019-00580-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,4,2]]},"references-count":26,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2019,12]]}},"alternative-id":["580"],"URL":"https:\/\/doi.org\/10.1007\/s13218-019-00580-7","relation":{},"ISSN":["0933-1875","1610-1987"],"issn-type":[{"type":"print","value":"0933-1875"},{"type":"electronic","value":"1610-1987"}],"subject":[],"published":{"date-parts":[[2019,4,2]]},"assertion":[{"value":"15 August 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 March 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 April 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}