Variational Approximated Restricted Maximum Likelihood Estimation for Spatial Data
This research considers a scalable inference for spatial data modeled through Gaussian intrinsic conditional autoregressive (ICAR) structures. The classical estimation method, restricted maximum likelihood (REML), requires repeated inversion and factorization of large, sparse precision matrices, which makes this computation costly. To sort this problem out, we propose a variational restricted maximum likelihood (VREML) framework that approximates the intractable marginal likelihood using a Gaussian variational distribution. By constructing an evidence lower bound (ELBO) on the restricted likelihood, we derive a computationally efficient coordinate-ascent algorithm for jointly estimating t...
| Authors | Debjoy Thakur |
|---|---|
| Primary category | stat.ML |
| Published | 8 April 2026 |
| Last updated at source | 4 August 2026 |
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