Monte Carlo Statistical Methods

By C. Robert, G. Casella. 2004

5,748 citations recorded by the source corpus.

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

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Citations in the corpus

Why Amazon's Ratings Might Mislead You: The Story of Herding Effects (2014, positive): “Given the current ratings summary x c , we apply Monte Carlo methods 19 to predict the ratings summary x c + t after including the next t new ratings (see the section Prediction Model in Supplementary Data).”
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).”
Uncertainty-guided Continual Learning with Bayesian Neural Networks (2019, methodology): “Markov Chain Monte Carlo (MCMC) sampling (Hastings, 1970) has been widely used and explored for this purpose, see (Robert & Casella, 2013) for different methods under this category.”
Batch Stationary Distribution Estimation (2020, background): “Unlike VPM, other post-processing meth-ods (Oates et al., 2017) require additional information about the target distribution (Robert and Casella, 2004).”
P3O: Policy-on Policy-off Policy Optimization (2019, background): “Such an importance sampling estimator is consistent but leads to increased variance because it discounts samples that have low likelihood under the new distribution p ( x ) (Robert and Casella, 2013; Elvira et al., 2018).”
Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam (2018, neutral): “5.2 in Robert & Casella (2005).”
Edward: A library for probabilistic modeling, inference, and criticism (2016, methodology): “Monte Carlo approximates the posterior using samples (Robert and Casella, 1999).”
Variational inference for Dirichlet process mixtures (2006, background): “Theoretical bounds on the mixing time are of little practical use, and there is no consensus on how to choose among the several empirical methods developed for this purpose (Robert and Casella, 1999).”
Particle Filtering in Geophysical Systems (2009, methodology): “4) MONTE CARLO METROPOLIS–HASTINGS An enormous amount of literature exists on the Metropolis–Hastings algorithm (Hastings 1970, see also Robert and Cassela 2004).”
Efficient sampling methods for truncated multivariate normal and student-t distributions subject to linear inequality constraints (2015, background): “More details can be seen in Chapter 2 of Robert and Casella (2004).”
Bayesian methods for neural networks (2000, methodology): “Carlo, hit and run algorithms, auxiliary variables and continuous time sampling methods (Besag et al., 1995; Brooks, 1998; Gilks et al., 1996; Meyn and Tweedie, 1993; Robert and Casella, 1999; Tierney, 1994).”
Multi-task Reinforcement Learning in Partially Observable Stochastic Environments (2009, background): “The weighting is a result from importance sampling (Robert and Casella 1999), and reflects the fact that r k t is obtained by following Π but the Monte Carlo integral (i.e., the empirical value function) is with respect to the RPR policy Θ.”