Bayesian inference for logistic models using Polya-Gamma latent variables (1205.0310v3)

Abstract: We propose a new data-augmentation strategy for fully Bayesian inference in models with binomial likelihoods. The approach appeals to a new class of Polya-Gamma distributions, which are constructed in detail. A variety of examples are presented to show the versatility of the method, including logistic regression, negative binomial regression, nonlinear mixed-effects models, and spatial models for count data. In each case, our data-augmentation strategy leads to simple, effective methods for posterior inference that: (1) circumvent the need for analytic approximations, numerical integration, or Metropolis-Hastings; and (2) outperform other known data-augmentation strategies, both in ease of use and in computational efficiency. All methods, including an efficient sampler for the Polya-Gamma distribution, are implemented in the R package BayesLogit. In the technical supplement appended to the end of the paper, we provide further details regarding the generation of Polya-Gamma random variables; the empirical benchmarks reported in the main manuscript; and the extension of the basic data-augmentation framework to contingency tables and multinomial outcomes.

• The paper introduces a Polya-Gamma data-augmentation technique that transforms binomial likelihoods into conditionally Gaussian forms for streamlined Gibbs sampling.
• It eliminates the need for numerical integration and Metropolis-Hastings by providing an exact, computationally efficient representation for logistic regression.
• Benchmark results demonstrate its superior sampling efficiency, especially in high-dimensional, hierarchical, and mixed-effect model applications.

Bayesian Inference for Logistic Models Using Polya-Gamma Latent Variables

The paper "Bayesian Inference for Logistic Models Using Polya-Gamma Latent Variables" introduces a novel data-augmentation technique for fully Bayesian inference in models with binomial likelihoods. This technique is based on a newly constructed class of Polya-Gamma (PG) distributions. The authors validate the versatility and efficacy of their method through several examples, including logistic regression, negative binomial regression, nonlinear mixed-effects models, and spatial models for count data. The proposed method addresses two significant challenges: it circumvents the need for analytic approximations, numerical integration, or Metropolis-Hastings algorithms, and it is computationally efficient compared to existing data-augmentation strategies.

Polya-Gamma Data-Augmentation Strategy

The core innovation lies in the introduction of the Polya-Gamma distribution and its application to logistical models. The Polya-Gamma distribution allows for a Gaussian mixture representation of binomial likelihoods, facilitating Gibbs sampling for posterior inference. Concretely, the authors provide the following key insights:

1. Polya-Gamma Distribution:
   • A random variable $X \sim PG(b,c)$ has a representation as an infinite sum of independent gamma random variables, which significantly simplifies Bayesian logistic regression by enabling conditionally Gaussian likelihoods.
   • The distribution satisfies an integral identity, where the binomial likelihood parameterized by log-odds is expressed as a mixture of Gaussians with respect to a Polya-Gamma distribution.
2. Gibbs Sampling Algorithm:
   • For logistic regression, the Gibbs sampler iteratively samples from the posterior distribution of the parameters given Polya-Gamma latent variables and vice versa.
   • The mean and covariance of the Gaussian distribution in the Gibbs sampler are functions of the design matrix, observed data, and Polya-Gamma latent variables.

Efficient Sampling of Polya-Gamma Variables

A significant contribution of the paper is the development of an efficient sampler for Polya-Gamma random variables:

• Acceptance-Reject Sampling: The Polya-Gamma $(1,z)$ random variable is sampled using an accept-reject method, where the acceptance rate is high, ensured by the properties of alternating-series expansion.
• Sum-of-Gammas Approximation: For larger shape parameters, the authors propose summing independent Polya-Gamma $(1,z)$ terms to obtain Polya-Gamma $(b,z)$. This method is linear in computation time with respect to the shape parameter, making it efficient in practice.

Extensive Benchmarking

The authors benchmarked their method against other state-of-the-art data-augmentation and Metropolis-Hastings strategies. Key observations include:

• Binary Logistic Regression: The Polya-Gamma method consistently outperformed other data-augmentation methods in terms of effective sample size and effective sampling rate, especially when parameters were numerous relative to the observations.
• Negative Binomial Models: Although the method suffered from the need to sum large numbers of Polya-Gamma random variables when the total count was high, it retained the highest effective sample sizes.
• Hierarchical and Mixed Models: The Polya-Gamma method showed significant advantages in models with complex prior structures, such as mixed-effects models, where conventional Metropolis-Hastings algorithms struggled without extensive tuning.

Theoretical and Practical Implications

The introduction of the Polya-Gamma data-augmentation strategy has both theoretical and practical implications:

• Theoretical: The paper demonstrates that binomial likelihoods can be represented as Gaussian mixtures using Polya-Gamma latent variables, leading to a straightforward Gibbs sampling algorithm. This representation is exact, conferring significant advantages over approximate methods.
• Practical: The technique is implemented in the R package BayesLogit, simplifying the application of Bayesian logistic regression and related models. The method's robustness and efficiency make it an attractive choice for practitioners dealing with high-dimensional logistic regression problems, particularly in hierarchical and mixed-model contexts.

Future Research Directions

Future research may focus on further optimizing the Polya-Gamma sampler, particularly for large count data, and extending the method to broader classes of models:

• Scalability: Enhancing the scalability of the Polya-Gamma sampler, for instance by leveraging GPU computation, could address the bottleneck in summing large numbers of random variables.
• Model Extensions: Exploring other applications within machine learning, such as multinomial logistic regression or more complex Bayesian hierarchical models, where the Polya-Gamma technique could simplify inferential procedures.

The paper provides a substantial advancement in Bayesian inference for logistic models, offering robust, efficient, and theoretically rigorous methods that can be easily adapted to a wide range of practical problems in modern statistical analysis.