7 citations · 15 across the 19 of their papers we have counts for
10 papers · 1 filter
Conjugating Variational Inference for Large Mixed Multinomial Logit Models and Consumer Choice
Weiben Zhang, Ruben Loaiza-Maya, Michael Stanley Smith +1
Heterogeneity in multinomial choice data is often accounted for using logit models with random coefficients. Such models are called "mixed", but they can be difficult to estimate f…
Robustifying Approximate Bayesian Computation
Chaya Weerasinghe, David T. Frazier, Ruben Loaiza-Maya +1
Approximate Bayesian computation (ABC) is one of the most popular "likelihood-free" methods. These methods have been applied in a wide range of fields by providing solutions to int…
ABC-based Forecasting in State Space Models
Chaya Weerasinghe, Ruben Loaiza-Maya, Gael M. Martin +1
Approximate Bayesian Computation (ABC) has gained popularity as a method for conducting inference and forecasting in complex models, most notably those which are intractable in som…
Implicit copula variational inference
Michael Stanley Smith, Rubén Loaiza-Maya
Key to effective generic, or "black-box", variational inference is the selection of an approximation to the target density that balances accuracy and speed. Copula models are promi…
Variational Bayes in State Space Models: Inferential and Predictive Accuracy
David T. Frazier, Ruben Loaiza-Maya, Gael M. Martin
Using theoretical and numerical results, we document the accuracy of commonly applied variational Bayes methods across a range of state space models. The results demonstrate that,…
Loss-Based Variational Bayes Prediction
David T. Frazier, Ruben Loaiza-Maya, Gael M. Martin +1
We propose a new approach to Bayesian prediction that caters for models with a large number of parameters and is robust to model misspecification. Given a class of high-dimensional…
Robust Approximate Bayesian Computation: An Adjustment Approach
David T. Frazier, Christopher Drovandi, Ruben Loaiza-Maya
We propose a novel approach to approximate Bayesian computation (ABC) that seeks to cater for possible misspecification of the assumed model. This new approach can be equally appli…
Fast and Accurate Variational Inference for Models with Many Latent Variables
Rubén Loaiza-Maya, Michael Stanley Smith, David J. Nott +1
Models with a large number of latent variables are often used to fully utilize the information in big or complex data. However, they can be difficult to estimate using standard app…
Focused Bayesian Prediction
Ruben Loaiza-Maya, Gael M. Martin, David T. Frazier
We propose a new method for conducting Bayesian prediction that delivers accurate predictions without correctly specifying the unknown true data generating process. A prior is defi…
Variational Bayes Estimation of Discrete-Margined Copula Models with Application to Time Series
Ruben Loaiza-Maya, Michael Stanley Smith
We propose a new variational Bayes estimator for high-dimensional copulas with discrete, or a combination of discrete and continuous, margins. The method is based on a variational…