{"type":"ScholarlyArticle","title":"Annealed Importance Sampling for Structure Learning in Bayesian Networks","authors":[{"name":"Teppo Mikael Niinimäki","slug":"teppo-mikael-niinimaki","given":"Teppo Mikael","family":"Niinimäki","affiliation":""},{"name":"Mikko Koivisto","slug":"mikko-koivisto","given":"Mikko","family":"Koivisto","affiliation":""}],"abstract":"We present a new sampling approach to Bayesian learning of the Bayesian network structure. Like some earlier sampling methods, we sample linear orders on nodes rather than directed acyclic graphs (DAGs). The key difference is that we replace the usual Markov chain Monte Carlo (MCMC) method by the method of annealed importance sampling (AIS). We show that AIS is not only competitive to MCMC in exploring the posterior, but also superior to MCMC in two ways: it enables easy and efficient parallelization, due to the independence of the samples, and lower-bounding of the marginal likelihood of the model with good probabilistic guarantees. We also provide a principled way to correct the bias due to order-based sampling, by implementing a fast algorithm for counting the linear extensions of a given partial order.","doi":null,"url":"https://ijcai.org/proceedings/2013/235","pdf":"https://ijcai.org/proceedings/2013/0235.pdf","markdown":"https://ijcai.org/proceedings/2013/235.md","bibtex":"https://ijcai.org/proceedings/2013/bibtex/235","volume":{"slug":"2013","title":"Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2013","url":"https://ijcai.org/proceedings/2013/","year":2013,"location":"Beijing, China","dates":"3–9 August 2013","editors":"Francesca Rossi","isbn":"978-1-57735-633-2","publisher":"AAAI Press / International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":235,"pages":{"first":1579,"last":1584},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}