A Survey of Bayesian Statistical Approaches for Big Data
arXiv:2006.04565 · doi:10.1007/978-3-030-42553-1
Abstract
The modern era is characterised as an era of information or Big Data. This has motivated a huge literature on new methods for extracting information and insights from these data. A natural question is how these approaches differ from those that were available prior to the advent of Big Data. We present a review of published studies that present Bayesian statistical approaches specifically for Big Data and discuss the reported and perceived benefits of these approaches. We conclude by addressing the question of whether focusing only on improving computational algorithms and infrastructure will be enough to face the challenges of Big Data.
References in corpus (30)
- Challenges of Big Data Analysis
- Big Data Computing and Clouds: Trends and Future Directions
- A Conceptual Introduction to Hamiltonian Monte Carlo
- Stochastic Gradient Hamiltonian Monte Carlo
- Gaussian Processes for Big Data
- Scalable Variational Gaussian Process Classification
- Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm
- Deep Gaussian Processes
- Austerity in MCMC Land: Cutting the Metropolis-Hastings Budget
- Speeding Up MCMC by Efficient Data Subsampling
- On Markov chain Monte Carlo methods for tall data
- Parallelizing MCMC via Weierstrass Sampler
- GPS-ABC: Gaussian Process Surrogate Approximate Bayesian Computation
- A Framework for Evaluating Approximation Methods for Gaussian Process Regression
- Accelerating ABC methods using Gaussian processes
- Firefly Monte Carlo: Exact MCMC with Subsets of Data
- Variational Sequential Monte Carlo
- Structured Bayesian Gaussian process latent variable model: applications to data-driven dimensionality reduction and high-dimensional inversion
- Practical Bayesian Modeling and Inference for Massive Spatial Datasets On Modest Computing Environments
- Pre-processing for approximate Bayesian computation in image analysis
- ABC-CDE: Towards Approximate Bayesian Computation with Complex High-Dimensional Data and Limited Simulations
- Divide-and-Conquer with Sequential Monte Carlo
- Variational consensus Monte Carlo
- Unbiased Bayes for Big Data: Paths of Partial Posteriors
- Integrating R and Hadoop for Big Data Analysis
- Bayesian Dyadic Trees and Histograms for Regression
- ABC model selection for spatial extremes models applied to South Australian maximum temperature data
- Streaming Variational Inference for Bayesian Nonparametric Mixture Models
- Average of Recentered Parallel MCMC for Big Data
- Multi-Statistic Approximate Bayesian Computation with Multi-Armed Bandits
Cited by in corpus (5)
- Uncertainty Quantification in Scientific Machine Learning: Methods, Metrics, and Comparisons
- Machine Learning in Orbit Estimation: a Survey
- A spectral surrogate model for stochastic simulators computed from trajectory samples
- Quantification of Deep Neural Network Prediction Uncertainties for VVUQ of Machine Learning Models
- Prediction and Uncertainty Quantification of SAFARI-1 Axial Neutron Flux Profiles with Neural Networks