Effective sample size for a mixture prior

2021 ◽  
pp. 109335
Author(s):  
Leonardo Egidi
Keyword(s):  
Sample Size ◽  
Effective Sample Size ◽  
Mixture Prior

scholarly journals Phylogenetic effective sample size

2016 ◽  
Vol 407 ◽  
pp. 371-386 ◽  
Author(s):  
Krzysztof Bartoszek
Keyword(s):  
Sample Size ◽  
Effective Sample Size

Prior Effective Sample Size of a Bayesian Model

2012 ◽  
pp. 1066-1069
Author(s):  
Satoshi Morita ◽  
Peter Thall
Keyword(s):  
Sample Size ◽  
Bayesian Model ◽  
Effective Sample Size

scholarly journals Locally Scaled and Stochastic Volatility Metropolis– Hastings Algorithms

Algorithms ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 351
Author(s):  
Wilson Tsakane Mongwe ◽  
Rendani Mbuvha ◽  
Tshilidzi Marwala
Keyword(s):  
Monte Carlo ◽  
Sample Size ◽  
Stochastic Volatility ◽  
Diffusion Processes ◽  
Predictive Performance ◽  
Jump Diffusion ◽  
Effective Sample Size ◽  
Jump Diffusion Processes ◽  
Current State ◽  
Mh Algorithm

Markov chain Monte Carlo (MCMC) techniques are usually used to infer model parameters when closed-form inference is not feasible, with one of the simplest MCMC methods being the random walk Metropolis–Hastings (MH) algorithm. The MH algorithm suffers from random walk behaviour, which results in inefficient exploration of the target posterior distribution. This method has been improved upon, with algorithms such as Metropolis Adjusted Langevin Monte Carlo (MALA) and Hamiltonian Monte Carlo being examples of popular modifications to MH. In this work, we revisit the MH algorithm to reduce the autocorrelations in the generated samples without adding significant computational time. We present the: (1) Stochastic Volatility Metropolis–Hastings (SVMH) algorithm, which is based on using a random scaling matrix in the MH algorithm, and (2) Locally Scaled Metropolis–Hastings (LSMH) algorithm, in which the scaled matrix depends on the local geometry of the target distribution. For both these algorithms, the proposal distribution is still Gaussian centred at the current state. The empirical results show that these minor additions to the MH algorithm significantly improve the effective sample rates and predictive performance over the vanilla MH method. The SVMH algorithm produces similar effective sample sizes to the LSMH method, with SVMH outperforming LSMH on an execution time normalised effective sample size basis. The performance of the proposed methods is also compared to the MALA and the current state-of-art method being the No-U-Turn sampler (NUTS). The analysis is performed using a simulation study based on Neal’s funnel and multivariate Gaussian distributions and using real world data modeled using jump diffusion processes and Bayesian logistic regression. Although both MALA and NUTS outperform the proposed algorithms on an effective sample size basis, the SVMH algorithm has similar or better predictive performance when compared to MALA and NUTS across the various targets. In addition, the SVMH algorithm outperforms the other MCMC algorithms on a normalised effective sample size basis on the jump diffusion processes datasets. These results indicate the overall usefulness of the proposed algorithms.

The Effective Sample Size of EHR-Derived Cohorts Under Biased Sampling

2021 ◽  
pp. 3-14
Author(s):  
Rebecca A. Hubbard ◽  
Carolyn Lou ◽  
Blanca E. Himes
Keyword(s):  
Sample Size ◽  
Biased Sampling ◽  
Effective Sample Size

scholarly journals An ARFIMA-based model for daily precipitation amounts with direct access to fluctuations

2020 ◽  
Vol 34 (10) ◽  
pp. 1487-1505
Author(s):  
Katja Polotzek ◽  
Holger Kantz
Keyword(s):  
Time Series ◽  
Sample Size ◽  
Confidence Intervals ◽  
Daily Precipitation ◽  
Rainfall Simulation ◽  
Direct Access ◽  
Effective Sample Size ◽  
Sample Mean ◽  
Daily Data ◽  
Synthetic Time Series

Abstract Correlations in models for daily precipitation are often generated by elaborate numerics that employ a high number of hidden parameters. We propose a parsimonious and parametric stochastic model for European mid-latitude daily precipitation amounts with focus on the influence of correlations on the statistics. Our method is meta-Gaussian by applying a truncated-Gaussian-power (tGp) transformation to a Gaussian ARFIMA model. The speciality of this approach is that ARFIMA(1, d, 0) processes provide synthetic time series with long- (LRC), meaning the sum of all autocorrelations is infinite, and short-range (SRC) correlations by only one parameter each. Our model requires the fit of only five parameters overall that have a clear interpretation. For model time series of finite length we deduce an effective sample size for the sample mean, whose variance is increased due to correlations. For example the statistical uncertainty of the mean daily amount of 103 years of daily records at the Fichtelberg mountain in Germany equals the one of about 14 years of independent daily data. Our effective sample size approach also yields theoretical confidence intervals for annual total amounts and allows for proper model validation in terms of the empirical mean and fluctuations of annual totals. We evaluate probability plots for the daily amounts, confidence intervals based on the effective sample size for the daily mean and annual totals, and the Mahalanobis distance for the annual maxima distribution. For reproducing annual maxima the way of fitting the marginal distribution is more crucial than the presence of correlations, which is the other way round for annual totals. Our alternative to rainfall simulation proves capable of modeling daily precipitation amounts as the statistics of a random selection of 20 data sets is well reproduced.

Determining Effective Sample Size to Calibrate a Transit Assignment Model: A Bayesian Perspective

2018 ◽  
Vol 2672 (8) ◽  
pp. 849-858 ◽  
Author(s):  
Mohadeseh Rahbar ◽  
Mark Hickman ◽  
Mahmoud Mesbah ◽  
Ahmad Tavassoli
Keyword(s):  
Sample Size ◽  
Effective Sample Size ◽  
Transit Assignment ◽  
Assignment Model

scholarly journals Testing for the significance of changes in television ratings

1990 ◽  
Vol 21 (3) ◽  
pp. 47-51
Author(s):  
Denny H. Meyer
Keyword(s):  
Sample Size ◽  
South African ◽  
Random Sample ◽  
Reliable Estimate ◽  
Effective Sample Size ◽  
Actual Sample ◽  
Rating Change ◽  
Television Ratings

South African television ratings are obtained from the AMPS meter panel. This panel must be viewed as a complex non random sample. For such samples the effective sample size differs from the actual sample size. It has been found that, when equal weights are assigned to strata, the most reliable estimate for effective sample size can be obtained by considering every household as a sample cluster. This estimate of effective sample size can be incorporated directly into a test for significant rating change. For convenience this test is implemented graphically.

Estimating multinomial effective sample size in catch-at-age and catch-at-size models

2017 ◽  
Vol 192 ◽  
pp. 66-83 ◽  
Author(s):  
Samuel B. Truesdell ◽  
James R. Bence ◽  
John M. Syslo ◽  
Mark P. Ebener
Keyword(s):  
Sample Size ◽  
Effective Sample Size

Effective sample size in a dichotomous process with noise

1996 ◽  
Vol 25 (6) ◽  
pp. 1233-1246 ◽  
Author(s):  
James Cussens
Keyword(s):  
Sample Size ◽  
Effective Sample Size
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