Name
Abstract | ||
|---|---|---|
We present a method for approximate inference for a broad class of non-conjugate probabilistic models. In particular, for the family of generalized linear model target densities we describe a rich class of variational approximating densities which can be best fit to the target by minimizing the Kullback-Leibler divergence. Our approach is based on using the Fourier representation which we show results in efficient and scalable inference. |
Year | Venue | Field |
|---|---|---|
2012 | NIPS | Affine transformation,Divergence,Linear model,Inference,Latent variable,Fast Fourier transform,Skew,Artificial intelligence,Probabilistic logic,Machine learning,Mathematics |
DocType | Citations | PageRank |
Conference | 6 | 0.73 |
References | Authors | |
15 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Challis, Edward | 1 | 46 | 3.35 |
| David Barber | 2 | 404 | 45.57 |