275 citations · 410 across the 25 of their papers we have counts for
6 papers · 1 filter
PICAR: An Efficient Extendable Approach for Fitting Hierarchical Spatial Models
Ben Seiyon Lee, Murali Haran
Hierarchical spatial models are very flexible and popular for a vast array of applications in areas such as ecology, social science, public health, and atmospheric science. It is c…
Reduced-dimensional Monte Carlo Maximum Likelihood for Latent Gaussian Random Field Models
Jaewoo Park, Murali Haran
Monte Carlo maximum likelihood (MCML) provides an elegant approach to find maximum likelihood estimators (MLEs) for latent variable models. However, MCML algorithms are computation…
A Function Emulation Approach for Doubly Intractable Distributions
Jaewoo Park, Murali Haran
Doubly intractable distributions arise in many settings, for example in Markov models for point processes and exponential random graph models for networks. Bayesian inference for t…
Bayesian Inference in the Presence of Intractable Normalizing Functions
Jaewoo Park, Murali Haran
Models with intractable normalizing functions arise frequently in statistics. Common examples of such models include exponential random graph models for social networks and Markov…
A Computationally Efficient Projection-Based Approach for Spatial Generalized Linear Mixed Models
Yawen Guan, Murali Haran
Inference for spatial generalized linear mixed models (SGLMMs) for high-dimensional non-Gaussian spatial data is computationally intensive. The computational challenge is due to th…
On automating Markov chain Monte Carlo for a class of spatial models
Murali Haran, Luke Tierney
Markov chain Monte Carlo (MCMC) algorithms provide a very general recipe for estimating properties of complicated distributions. While their use has become commonplace and there is…