Comparing Gaussian and Monte Carlo Statistics
Comparing forecast accuracy: A Monte Carlo investigation
... test statistics. Second, if models are nested, the statistics based on average comparisons of prediction errors have a degenerate limiting variance under the null hypothesis and they are not asymptotically ...
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A Monte Carlo Comparison of Robust MANOVA Test Statistics
... rates compared to their non-trimmed counterparts. However, these differences were consistently very small, and generally did not offer a substantive advantage over the non-trimmed test statistics. Note that power ...
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Deep Gaussian processes using expectation propagation and Monte Carlo methods
... Gaussian processes are non-parametric machine learning models that present advan- tages over other models. They provide not only a prediction about the output value, but they also provide the uncertainty of such ...
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NLOS mitigation in indoor localization by marginalized Monte Carlo Gaussian smoothing
... on Gaussian filtering and smoothing in non- linear/Gaussian systems, together with the sigma-point- based approximation of the multidimensional integrals in the conceptual solution, being computationally ...
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A Hybrid Monte Carlo Sampling Filter for Non-Gaussian Data Assimilation
... Virginia Polytechnic Institute and State University, Blacksburg, Virginia, 24060, USA ∗ Correspondence: Email: [email protected]; Tel: +1-540-231-6186. Abstract: Data assimilation combines information from models, ...
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Comparing Alternative Output-Gap Estimators: A Monte Carlo Approach
... Abstract The author evaluates the ability of a variety of output-gap estimators to accurately measure the output gap in a model economy. A small estimated model of the Canadian economy is used to generate artificial ...
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Monte Carlo simulations of the Ising model on a square lattice with random Gaussian interactions
... 1. Introduction The Ising model, named after Ernst Ising [1] and studied at least 5 years earlier (in early 1920s) by Lenz [2], offers an excellent testing ground for studies of the physics of classical and quantum phase ...
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Four essays on sequential Monte Carlo and quasi-Monte Carlo methods
... is Gaussian, then the Fisher information matrix is singular and the Bayes factor might no longer be a consistent criterion to discriminate between the two competing ...through Monte Carlo simulations ...
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Monte Carlo methods
... In practice, either a reasonable choice for q is available, or not. The first case occurs when, e.g., π is almost unimodal and concentrates its mass on a small region of X . A Gaussian centered at this small ...
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Small Sample Properties of Certain Cointegration Test Statistics: A Monte Carlo Study
... Both tests fare equally badly or equally well, except in the case of PC6 (which has two unit roots and two large stationary roots) where the conformity test rejects the null of two zero [r] ...
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Automatic kernel selection for Gaussian processes regression with approximate Bayesian computation and sequential Monte Carlo
... The article starts out with an introduction to the GPs and approximate Bayesian computation based on Sequential Monte Carlo (ABC-SMC) algorithm and the selection of the differ- ent hyperparameters required ...
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Estimation of Reliability Function for Inverse Gaussian Distribution Model with Application by Using Monte Carlo Simulation
... Chapter Three Monte-Carlo Application 3.1 Introduction After constructing a mathematical model for the problem under consideration, the next step is to derive a solution. There are analytic and numerical ...
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Particle rejuvenation of Rao Blackwellized sequential Monte Carlo smoothers for conditionally linear and Gaussian models
... sequential Monte Carlo methods to approximate smoothing distributions in conditionally linear and Gaussian state spaces in a common unifying ...some Monte Carlo experiments with ...
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Estimating Gaussian Mixture Autoregressive model with Sequential Monte Carlo algorithm: A parallel GPU implementation
... Second, given past history, the conditional distribution of underlying time series can be multimodal. Third, the MAR is capable of capturing the con- ditional heteroscedasticity, which is common in many nonlinear time ...
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Multiscale modeling of a rectifying bipolar nanopore : comparing Poisson Nernst Planck to Monte Carlo
... parameters. Comparing to NP+LEMC results makes it possible to focus on the ap- proximations applied in the statistical mechanical part of the PNP theory (the PB theory), because NP is common in ...
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Wavelet Monte Carlo dynamics
... allowing the noise contribution to be ignored, namely Eq. (5.12), is an application of a more general result for the overdamped Langevin equations. This work looked more closely at the polymer’s velocity autocorrelation ...
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The jackknife in Monte Carlo studies
... 4 Summary We think that jackknife standard errors should be routinely used in reporting Monte Carlo results. They are much easier to obtain than those from the standard delta method approach, especially ...
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CONTROLLED SEQUENTIAL MONTE CARLO
... by comparing the behaviour of the nor- malizing constant estimates across time with those when the optimal policy is applied, as detailed in Proposition ...
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Sequential Monte Carlo with transformations
... approaches, although we note that this is achieved at a higher computational cost due to the sum in the denomina- tor of the weight updates, this can be observed in Fig. 2c which shows the cumulative number of ...
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Multistep Kinetic Monte Carlo
... This drop happened even when distance from the boundary was taken into account in the parameters of the Gaussion distribution. Gaussian random walks only appoximate the diffusion equation well if the domain is ...
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