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A survey of Monte Carlo methods for parameter estimation
Statistical signal processing applications usually require the estimation of some parameters
of interest given a set of observed data. These estimates are typically obtained either by …
of interest given a set of observed data. These estimates are typically obtained either by …
[HTML][HTML] A tutorial on bridge sampling
The marginal likelihood plays an important role in many areas of Bayesian statistics such as
parameter estimation, model comparison, and model averaging. In most applications …
parameter estimation, model comparison, and model averaging. In most applications …
bridgesampling: An R package for estimating normalizing constants
Statistical procedures such as Bayes factor model selection and Bayesian model averaging
require the computation of normalizing constants (eg, marginal likelihoods). These …
require the computation of normalizing constants (eg, marginal likelihoods). These …
Adaptive importance sampling: The past, the present, and the future
A fundamental problem in signal processing is the estimation of unknown parameters or
functions from noisy observations. Important examples include localization of objects in …
functions from noisy observations. Important examples include localization of objects in …
Pareto smoothed importance sampling
Importance weighting is a general way to adjust Monte Carlo integration to account for
draws from the wrong distribution, but the resulting estimate can be highly variable when the …
draws from the wrong distribution, but the resulting estimate can be highly variable when the …
Importance nested sampling and the MultiNest algorithm
Bayesian inference involves two main computational challenges. First, in estimating the
parameters of some model for the data, the posterior distribution may well be highly multi …
parameters of some model for the data, the posterior distribution may well be highly multi …
[كتاب][B] Introducing monte carlo methods with r
CP Robert, G Casella, G Casella - 2010 - Springer
The purpose of this book is to provide a self-contained entry into Monte Carlo computational
techniques. First and foremost, it must not be confused with a programming addendum to …
techniques. First and foremost, it must not be confused with a programming addendum to …
Survey of sampling-based methods for uncertainty and sensitivity analysis
Sampling-based methods for uncertainty and sensitivity analysis are reviewed. The
following topics are considered:(i) definition of probability distributions to characterize …
following topics are considered:(i) definition of probability distributions to characterize …
VIVACE (Vortex Induced Vibration Aquatic Clean Energy): A new concept in generation of clean and renewable energy from fluid flow
MM Bernitsas, K Raghavan, Y Ben-Simon… - 2008 - asmedigitalcollection.asme.org
Any device aiming to harness the abundant clean and renewable energy from ocean and
other water resources must have high energy density, be unobtrusive, have low …
other water resources must have high energy density, be unobtrusive, have low …
Importance sampling: a review
We provide a short overview of importance sampling—a popular sampling tool used for
Monte Carlo computing. We discuss its mathematical foundation and properties that …
Monte Carlo computing. We discuss its mathematical foundation and properties that …