Monte Carlo Statistical Methods

2005

Subfields: Statistics and Probability, Probabilistic & Bayesian Methods

Citations in the corpus

• Variational Inference: A Review for Statisticians (2016, background): “In a similar vein, Tan et al. (2014) extend stochastic variational inference (SVI) to generalized linear mixed models.”
• Control functionals for Monte Carlo integration (2014, methodology): “c intractability of such models has inspired the development of sophisticated Monte Carlo methodologies to facilitate computation. Many of these approaches ultimately rely on Monte Carlo integration (Robert and Casella, 2004). Address for correspondence: Dept. Statistics, Zeeman Building, University”
• What regularized auto-encoders learn from the data-generating distribution (2012, background): “ future work, but we show that in principle the local mean or estimated score is already sufficient to sample from the underlying density. We start from the presentation of such diffusion equations in Robert and Casella (1999). The Langevin diffusion X tis defined by the stochastic differential equati”
• Analyzing Inverse Problems with Invertible Neural Networks (2018, methodology): “tunately, exact Bayesian treatment of real-world problems is usually intractable. The most common (but expensive) solution is to resort to sampling, typically by a variant of Markov Chain Monte Carlo [36, 11]. If a model y = s(x) for the forward process is available, approximate Bayesian computation”
• MCMC-Driven Adaptive Multiple Importance Sampling (2015, methodology): “In order to solve these issues, several works are devoted to the design of adaptive IS (AIS) schemes [12], where the proposal density is updated by learning from all the previously generated samples.”
• Bayesian Spatial Modeling of Extreme Precipitation Return Levels (2007, methodology): “For background on MCMC methods, see Robert and Casella (1999), and for a reference on Bayesian inference via MCMC, see Gelman et al. (2003).”
• Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations (2009, unknown): “Latent Gaussian models have a numerous and wide ranging list of applications; most structured Bayesian models are in fact of this form; see for example Fahrmeir and Tutz (2001), Gelman et al. (2004) and Robert and Casella (1999).”
• ANALYSIS OF SPDES ARISING IN PATH SAMPLING PART II: THE NONLINEAR CASE (2006, methodology): “eA˜ ∈ R m×d is arbitraryandB˜ ∈ R is invertible,and Wy is a standard m-dimensional Brownian motion. The methodology proposed in [SVW04] is to extend the finite di mensional Langevin sampling technique [RC99] to infinite dimensional probl ems such as those listed in 1. to 3. This leads to SPDEs which a”
• Multivariate Bayesian analysis of atmosphere–ocean general circulation models (2007, unknown): “The essence of the MCMC approach is to simulate complex joint probability distributions by sampling from a Markov chain with a stationary, ergodic distribution that is identical to the posterior distribution (see also Gilks et al. 1996; Robert and Casella 1999).”
• Perception, Action, and Utility: The Tangled Skein (2012, background): “For example, Shuler and Bear (2006) trained rats to associate monocular stimulation with liquid reward, and found that V1 neurons altered their firing patterns to predict the timing of reward.”
• A Group-Theoretic Framework for Data Augmentation (2019, methodology): “Variance reduction techniques are widely used in Monte Carlo methods (Robert and Casella, 2013).”