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Learning Causal Bayesian Network Structures from Experimental Data
| Content Provider | Semantic Scholar |
|---|---|
| Author | Baxter, Byron Ellis Wong, Wing Hung |
| Copyright Year | 2006 |
| Abstract | We propose a method for the computational inference of directed acyclic graphical structures given data from experimental interventions. Order-space MCMC, equi-energy sampling, importance weighting and stream-based computation are combined to create a fast algorithm for learning causal Bayesian network structures. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://web.stanford.edu/group/wonglab/doc/EllisWong-061025.pdf |
| Alternate Webpage(s) | http://web.stanford.edu/group/wonglab/doc/EllisWong-061025.pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Algorithm Bayesian network Causal filter Color Computation Computation (action) Directed acyclic graph Directed graph Experiment Graph - visual representation Inference Large Markov chain Monte Carlo Missing data Partial Reading (activity) Relaxation Sampling (signal processing) Sampling - Surgical action Signal Transduction Transduction (machine learning) |
| Content Type | Text |
| Resource Type | Article |
