Structure Discovery in Bayesian Networks by Sampling Partial Orders
By Teppo Niinimaki, Pekka Parviainen, Mikko Koivisto. 2016
55 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Physical and Theoretical Chemistry, Probabilistic & Bayesian Methods
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Yoshua Bengio
- Simon Lacoste-Julien
- Eric P. Xing
- Anirudh Goyal
- Stefan Bauer
- Bernhard Schölkopf
- Alexandre Lacoste
- Jonas Rothfuss
In Physical and Theoretical Chemistry
In Probabilistic & Bayesian Methods
- Yoshua Bengio
- Anirudh Goyal
- Eric P. Xing
- Tristan Deleu
- Bryon Aragam
- Stefan Bauer
- Alexandre Lacoste
- Simon Lacoste-Julien
Citations in the corpus
- Bayesian Structure Learning with Generative Flow Networks (2022, methodology): “This was further refined by either modifying the underlying space of the Markov chain (Kuipers and Moffa, 2017; Niinimäki et al., 2016), or its local moves (Mansinghka et al., 2006; Eaton and Murphy, 2007a; Kuipers et al., 2021).”
- DAGs with NO TEARS: Continuous Optimization for Structure Learning (2018, methodology): “Other approaches that avoid optimizing (4) directly include constraint-based methods (Spirtes and Glymour, 1991; Spirtes et al., 2000), hybrid methods (Tsamardinos et al., 2006; G´amez et al., 2011), and Bayesian methods (Ellis and Wong, 2008; Zhou, 2011; Niinim¨aki et al., 2016).”
- Variational Causal Networks: Approximate Bayesian Inference over Causal Structures (2021, methodology): “A popular choice for such an approach is the Markov Chain Monte Carlo (MCMC) with suitably chosen energy functions and heuristics [8, 26, 25, 22, 14, 9].”
- Towards Scalable Bayesian Learning of Causal DAGs (2020, methodology): “The multiset of node indegrees is unique for DAGs in the same equivalence class.”