2023-07-02-jiang23b.md

abstract	openreview	title	video	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Partial orders are a natural model for the social hierarchies that may constrain “queue-like” rank-order data. However, the computational cost of counting the linear extensions of a general partial order on a ground set with more than a few tens of elements is prohibitive. Vertex-series-parallel partial orders (VSPs) are a subclass of partial orders which admit rapid counting and represent the sorts of relations we expect to see in a social hierarchy. However, no Bayesian analysis of VSPs has been given to date. We construct a marginally consistent family of priors over VSPs with a parameter controlling the prior distribution over VSP depth. The prior for VSPs is given in closed form. We extend an existing observation model for queue-like rank-order data to represent noise in our data and carry out Bayesian inference on “Royal Acta” data and Formula 1 race data. Model comparison shows our model is a better fit to the data than Plackett-Luce mixtures, Mallows mixtures, and “bucket order” models and competitive with more complex models fitting general partial orders.	ANMagI9jQ4Z	Bayesian inference for vertex-series-parallel partial orders	https://youtu.be/KcnHhCS4vKk	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	jiang23b	0	Bayesian inference for vertex-series-parallel partial orders	995	1004	995-1004	995	false	Jiang, Chuxuan and Nicholls, Geoff K. and Lee, Jeong Eun	given family Chuxuan Jiang given family Geoff K. Nicholls given family Jeong Eun Lee	2023-07-02		Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence	216	inproceedings	date-parts 2023 7 2	https://proceedings.mlr.press/v216/jiang23b/jiang23b.pdf	label link Supplementary PDF https://proceedings.mlr.press/v216/jiang23b/jiang23b-supp.pdf