Bayesian data analysis.

By John K. Kruschke. 2010

11,575 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability, Probabilistic & Bayesian Methods

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Citations in the corpus

Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks (2017, methodology): “We applied this to the “Eight School” example from Gelman et al. (2014).”
On the Equivalence between Kernel Quadrature Rules and Random Feature Expansions (2015, background): “…of high-dimensional integrals is one of the core computational tasks in many areas of machine learning, signal processing and more generally applied mathematics, in particular in the context of Bayesian inference (Gelman, 2004), or the study of complex systems (Robert and Casella, 2005).”
Deep Factors for Forecasting (2019, methodology): “A similar mixed approach is used in Hierarchical Bayesian (Gelman et al., 2013) methods, which combine global and local models to jointly model a population of related statistical problems.”
Recasting Gradient-Based Meta-Learning as Hierarchical Bayes (2018, methodology): “(2) Maximizing (2) as a function of θ gives a point estimate for θ, an instance of a method known as empirical Bayes (Bernardo & Smith, 2006; Gelman et al., 2014) due to its use of the data to estimate the parameters of the prior distribution.”
Verified Uncertainty Calibration (2019, methodology): “Besides calibration, other ways of producing and quantifying uncertainties include Bayesian methods [40] and conformal prediction [41, 42].”
GenDICE: Generalized Offline Estimation of Stationary Values (2020, background): “Amortized SVGD (Wang & Liu, 2016) and Adversarial MCMC (Song et al., 2017; Li et al., 2019) alleviate this issue via combining with neural network, but they still interact with the transition operator directly, i.e., in an on-policy setting.”
Informing sequential clinical decision-making through reinforcement learning: an empirical study (2010, methodology): “This is done by plugging in parameter values sampled from the posterior distribution and a sampled random error value into the complete data model (Gelman et al. 1995).”
The No-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo (2011, methodology): “To evaluate NUTS and HMC, we used the two algorithms to sample from four target distributions, one of which was synthetic and the other three of which are posterior distributions arising from real datasets.”
Posterior calibration and exploratory analysis for natural language processing models (2015, methodology): “Our posterior simulation approach for exploratory and error analysis relates to posterior predictive checking (Gelman et al., 2013), which analyzes a posterior to test model assumptions; Mimno and Blei (2011) density estimation.”
Automatic Differentiation Variational Inference (2016, methodology): “When we cycle through these steps, we find expressive, interpretable, and useful models (Gelman et al., 2013; Blei, 2014).”
Bayesian Multitask Learning with Latent Hierarchies (2009, background): “(The idea of decomposing the covariance comes from [11], section 19.”
Automated Scalable Bayesian Inference via Hilbert Coresets (2017, positive): “For all models, we evaluate two metrics of posterior quality: negative log-likelihood on the held-out test set, averaged over posterior MCMC samples; and 1-Wasserstein distance of the posterior samples to samples obtained from running MCMC on the full data set.”
Venture: a higher-order probabilistic programming platform with programmable inference (2014, methodology): “Probabilistic modeling and approximate Bayesian inference have proven to be powerful tools in multiple fields, from machine learning (Bishop, 2006) and statistics (Green et al., 2003; Gelman et al., 1995) to robotics (Thrun et al.”
Learning with Submodular Functions: A Convex Optimization Perspective (2011, neutral): “, [75]).”
Edward: A library for probabilistic modeling, inference, and criticism (2016, methodology): “The model assumes a latent linear relationship between these two random variables (Gelman et al., 2013).”