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Adaptive Metropolis Sampling with Product Distributions
| Content Provider | Semantic Scholar |
|---|---|
| Author | Wolpert, David Hilton Lee, Chiu Fan |
| Copyright Year | 2005 |
| Abstract | The Metropolis-Hastings (MH) algorithm is a way to sample a provided target distribution π(x). It works by repeatedly sampling a separate proposal distribution T (x, x′) to generate a random walk {x(t)}. We consider a modification of the MH algorithm in which T is dynamically updated during the walk. The update at time t uses the {x(t′ < t)} to estimate the product distribution that has the least Kullback-Leibler distance to π. That estimate is the information-theoretically optimal mean-field approximation to π. We demonstrate through computer experiments that our algorithm produces samples that are superior to those of the conventional MH algorithm. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://ic.arc.nasa.gov/m/pub-archive/864h/0864%20(Wolpert).pdf |
| Alternate Webpage(s) | http://www.ic.arc.nasa.gov/m/pub/864h/0864%20(Wolpert).pdf |
| Alternate Webpage(s) | https://ti.arc.nasa.gov/m/pub-archive/864h/0864%20(Wolpert).pdf |
| Alternate Webpage(s) | http://ti.arc.nasa.gov/m/pub/864h/0864%20(Wolpert).pdf |
| Alternate Webpage(s) | http://ic.arc.nasa.gov/m/pub/864h/0864%20(Wolpert).pdf |
| Alternate Webpage(s) | http://www.ic.arc.nasa.gov/m/pub-archive/864h/0864%20(Wolpert).pdf |
| Alternate Webpage(s) | http://ti.arc.nasa.gov/m/pub-archive/864h/0864%20(Wolpert).pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
