DeepAI AI Chat
Log In Sign Up

Bayesian inference on high-dimensional multivariate binary data

by   Antik Chakraborty, et al.

It has become increasingly common to collect high-dimensional binary data; for example, with the emergence of new sampling techniques in ecology. In smaller dimensions, multivariate probit (MVP) models are routinely used for inferences. However, algorithms for fitting such models face issues in scaling up to high dimensions due to the intractability of the likelihood, involving an integral over a multivariate normal distribution having no analytic form. Although a variety of algorithms have been proposed to approximate this intractable integral, these approaches are difficult to implement and/or inaccurate in high dimensions. We propose a two-stage Bayesian approach for inference on model parameters while taking care of the uncertainty propagation between the stages. We use the special structure of latent Gaussian models to reduce the highly expensive computation involved in joint parameter estimation to focus inference on marginal distributions of model parameters. This essentially makes the method embarrassingly parallel for both stages. We illustrate performance in simulations and applications to joint species distribution modeling in ecology.


page 20

page 29


Split-BOLFI for for misspecification-robust likelihood free inference in high dimensions

Likelihood-free inference for simulator-based statistical models has rec...

Amortized Bayesian Inference for Models of Cognition

As models of cognition grow in complexity and number of parameters, Baye...

Computation of quantile sets for bivariate data

Algorithms are proposed for the computation of set-valued quantiles and ...

Inference and Sampling for Archimax Copulas

Understanding multivariate dependencies in both the bulk and the tails o...

On predictive inference for intractable models via approximate Bayesian computation

Approximate Bayesian computation (ABC) is commonly used for parameter es...

Extremes in High Dimensions: Methods and Scalable Algorithms

Extreme-value theory has been explored in considerable detail for univar...

Generalized Matrix Factorization

Unmeasured or latent variables are often the cause of correlations betwe...