scholarly journals Model Averaging for Semivariogram Model Parameters

10.5772/52339 ◽  
2013 ◽  
Author(s):  
Asim Biswas ◽  
Bing Cheng
Keyword(s):  
Model Averaging ◽  
Model Parameters ◽  
Semivariogram Model

scholarly journals A Flexible Data-Driven Framework for COVID-19 Case Forecasting Deployed in a Developing-world Public Health Setting

2021 ◽  
Author(s):  
Sansiddh Jain ◽  
Avtansh Tiwari ◽  
Nayana Bannur ◽  
Ayush Deva ◽  
Siddhant Shingi ◽  
...  
Keyword(s):  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Ground Truth ◽  
Model Parameters ◽  
Forecasting System ◽  
Critical Components ◽  
Approximate Bayesian ◽  
Operational Constraints ◽  
Best Fit ◽  
Public Health Setting

Forecasting infection case counts and estimating accurate epidemiological parameters are critical components of managing the response to a pandemic. This paper describes a modular, extensible framework for a COVID-19 forecasting system, primarily deployed in Mumbai and Jharkhand, India. We employ a variant of the SEIR compartmental model motivated by the nature of the available data and operational constraints. We estimate best-fit parameters using sequential Model-Based Optimization (SMBO) and describe the use of a novel, fast, and approximate Bayesian model averaging method (ABMA) for parameter uncertainty estimation that compares well with a more rigorous Markov Chain Monte Carlo (MCMC) approach in practice. We address on-the-ground deployment challenges such as spikes in the reported input data using a novel weighted smooth-ing method. We describe extensive empirical analyses to evaluate the accuracy of our method on ground truth as well as against other state-of-the-art approaches. Finally, we outline deployment lessons and describe how inferred model parameters were used by government partners to interpret the state of the epidemic and how model forecasts were used to estimate staffing and planning needs essential for addressing COVID-19 hospital burden.

scholarly journals Bayesian Model Averaging and Prior Sensitivity in Stochastic Frontier Analysis

Econometrics ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 13 ◽  
Author(s):  
Kamil Makieła ◽  
Błażej Mazur
Keyword(s):  
Latent Variables ◽  
Bayesian Model ◽  
Stochastic Frontier Analysis ◽  
Bayesian Model Averaging ◽  
Scale Parameter ◽  
Stochastic Frontier ◽  
Model Averaging ◽  
Model Parameters ◽  
Frontier Analysis ◽  
Stochastic Parameters

This paper discusses Bayesian model averaging (BMA) in Stochastic Frontier Analysis and investigates inference sensitivity to prior assumptions made about the scale parameter of (in)efficiency. We turn our attention to the “standard” prior specifications for the popular normal-half-normal and normal-exponential models. To facilitate formal model comparison, we propose a model that nests both sampling models and generalizes the symmetric term of the compound error. Within this setup it is possible to develop coherent priors for model parameters in an explicit way. We analyze sensitivity of different prior specifications on the aforementioned scale parameter with respect to posterior characteristics of technology, stochastic parameters, latent variables and—especially—the models’ posterior probabilities, which are crucial for adequate inference pooling. We find that using incoherent priors on the scale parameter of inefficiency has (i) virtually no impact on the technology parameters; (ii) some impact on inference about the stochastic parameters and latent variables and (iii) substantial impact on marginal data densities, which are crucial in BMA.

scholarly journals Temporal Weighted Averaging for Asynchronous Federated Intrusion Detection Systems

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Shaashwat Agrawal ◽  
Aditi Chowdhuri ◽  
Sagnik Sarkar ◽  
Ramani Selvanambi ◽  
Thippa Reddy Gadekallu
Keyword(s):  
Intrusion Detection ◽  
Detection System ◽  
Model Averaging ◽  
Global Model ◽  
Weighted Averaging ◽  
Model Parameters ◽  
Detection Systems ◽  
Performance Accuracy ◽  
Temporal Model ◽  
Iot Devices

Federated learning (FL) is an emerging subdomain of machine learning (ML) in a distributed and heterogeneous setup. It provides efficient training architecture, sufficient data, and privacy-preserving communication for boosting the performance and feasibility of ML algorithms. In this environment, the resultant global model produced by averaging various trained client models is vital. During each round of FL, model parameters are transferred from each client device to the server while the server waits for all models before it can average them. In a realistic scenario, waiting for all clients to communicate their model parameters, where client models are trained on low-power Internet of Things (IoT) devices, can result in a deadlock. In this paper, a novel temporal model averaging algorithm is proposed for asynchronous federated learning (AFL). Our approach uses a dynamic expectation function that computes the number of client models expected in each round and a weighted averaging algorithm for continuous modification of the global model. This ensures that the federated architecture is not stuck in a deadlock all the while increasing the throughput of the server and clients. To implicate the importance of asynchronicity in cybersecurity, the proposed algorithm is tested using NSL-KDD intrusion detection system datasets. The performance accuracy of the global model is about 99.5% on the dataset, outperforming traditional FL models in anomaly detection. In terms of asynchronicity, we get an increased throughput of almost 10.17% for every 30 timesteps.

scholarly journals Computing and Using Inclusion Bayes Factors for Mixed Fixed and Random Effect Diffusion Decision Models

2021 ◽  
Author(s):  
Udo Boehm ◽  
Nathan J. Evans ◽  
Quentin Frederik Gronau ◽  
Dora Matzke ◽  
Eric-Jan Wagenmakers ◽  
...  
Keyword(s):  
Bayesian Model Averaging ◽  
Random Effect ◽  
Quantitative Description ◽  
Model Averaging ◽  
Cognitive Models ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Data Sets ◽  
Bayesian Hierarchical ◽  
Selective Influence

Cognitive models provide a substantively meaningful quantitative description of latent cognitive processes. The quantitative formulation of these models supports cumulative theory building and enables strong empirical tests. However, the non-linearity of these models and pervasive correlations among model parameters pose special challenges when applying cognitive models to data. Firstly, estimating cognitive models typically requires large hierarchical data sets that need to be accommodated by an appropriate statistical structure within the model. Secondly, statistical inference needs to appropriately account for model uncertainty to avoid overconfidence and biased parameter estimates. In the present work we show how these challenges can be addressed through a combination of Bayesian hierarchical modelling and Bayesian model averaging. To illustrate these techniques, we apply the popular diffusion decision model to data from a collaborative selective influence study.

scholarly journals Fractal dimension and geostatistical parameters for soil microrelief as a function of cumulative precipitation

2010 ◽  
Vol 67 (1) ◽  
pp. 78-83 ◽  
Author(s):  
Eva Vidal Vázquez ◽  
Sidney Rosa Vieira ◽  
Isabella Clerici De Maria ◽  
Antonio Paz González
Keyword(s):  
Surface Roughness ◽  
Fractal Dimension ◽  
Agricultural Practices ◽  
Soil Surface ◽  
Soil Tillage ◽  
Model Parameters ◽  
Semivariogram Model ◽  
Scale Free ◽  
Chisel Plow ◽  
Disc Level

Surface roughness isinfluenced by type and intensity of soil tillage among other factors, and it changes considerably with rain. In microrelief studies the advantages of using indices such as the fractal dimension, D, and the crossover length, l, is that they allow the partition of the roughness characteristics into properties that depend purely on the scale and on a scale free component, respectively. On the other hand, some geostatistical parameters may provide different ways to understand soil surface variability not addressed with fractal parameters. Changes in fractal dimension and semivariogram parameters for surface roughness evolution were evaluated as a function of cumulative rainfall on Oxisol samples over six tillage treatments, namely, disc harrow, disc plow, chisel plow, disc harrow+disc level, disc plow+disc level and chisel plow+disc level. Measurements were taken in each tillage treatment after rainfall events yielding a total of 48 experimental surfaces measured with a pin microrelief meter. The plot had 135 cm by 135 cm and the sample spacing was 25 mm. Trends due to plot slope component with its concavities and convexities and to agricultural practices were removed from field data sets. A semivariogram model was fitted to each of the surfaces and the model parameters were analyzed and related to the fractal dimension, D, and crossover length, l. A relationship was found between the fractal dimension, D, and semivariogram model parameters. The cross over length, l,did not show as strong relationships with the semivariogram model parameters, even though there was a power relation between D and l.

scholarly journals Factors Affecting Life Expectancy in East Java: Predictions with A Bayesian Model Averaging Approach

2021 ◽  
Vol 5 (2) ◽  
pp. 283-295
Author(s):  
Harun Al Azies ◽  
Vivi Mentari Dewi
Keyword(s):  
Life Expectancy ◽  
Regression Model ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Infant Mortality Rate ◽  
Model Parameters ◽  
Data Set ◽  
Expectancy Value ◽  
Factors Affecting

This study predicts the factors that influence life expectancy in East Java, Indonesia. In particular, this study compares the prediction results between the linear regression model and the Bayesian Model Averaging (BMA). The study used a 2015 data set from the Central Bureau of Statistics (BPS) of the province of East Java.The results of data exploration show that the life expectancy in East Java is 70.68 years, the Bondowoso regency is the region with the lowest life expectancy at 65.73 years and the city of Surabaya is the area with the highest life expectancy value in East Java, which is 73.85 years.The results of the inference study indicate that the factors that are expected to affect life expectancy in East Java are the infant mortality rate and the illiteracy rate of the population aged 10 and over.The results of the comparison between the BMA and the regression show that the BMA is a better model for predicting the factors that affect life expectancy in East Java than the regression model because the BMA model can estimate the parameters more efficiently by estimating the model parameters based on the standard error value.

scholarly journals Simulation of Gross Primary Productivity Using Multiple Light Use Efficiency Models

Land ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 329
Author(s):  
Jun Zhang ◽  
Xufeng Wang ◽  
Jun Ren
Keyword(s):  
Remote Sensing ◽  
Primary Productivity ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Gross Primary Productivity ◽  
Accurate Estimation ◽  
Model Parameters ◽  
Light Use Efficiency ◽  
Multiple Model ◽  
Use Efficiency

Gross primary productivity (GPP) is the most basic variable in a carbon cycle study that determines the carbon that enters the ecosystem. The remote sensing-based light use efficiency (LUE) model is one of the primary tools that is currently used to estimate the GPP at the regional scale. Many remote sensing-based GPP models have been developed in the last several decades, and these models have been well evaluated at some sites. However, an accurate estimation of the GPP remains challenging work using LUE models because of uncertainties in the model caused by model parameters, model forcing, and vegetation spatial heterogeneity. In this study, five widely used LUE models, Glo-PEM, VPM, EC-LUE, the MODIS GPP algorithm, and C-fix, were selected to simulate the GPP of the Heihe River Basin forced using in situ measurements. A multiple-model averaging method, Bayesian model averaging (BMA), was used to combine the five models to obtain a more reliable GPP estimation. The BMA was trained using carbon flux data from five eddy covariance towers located at dominant vegetation types in the study area. Generally, the BMA method performed better than any single LUE model. From the case study in the study area, it is indicated that the trained BMA is an efficient method to combine multiple LUE models and can improve the GPP simulation accuracy.

Instrumental and Ideological Union Commitment: Longitudinal Assessment of Construct Validity

2001 ◽  
Vol 17 (2) ◽  
pp. 98-111 ◽  
Author(s):  
Anders Sjöberg ◽  
Magnus Sverke
Keyword(s):  
Construct Validity ◽  
Temporal Stability ◽  
Strong Support ◽  
Internal Consistency Reliability ◽  
Measurement Model ◽  
Two Dimensions ◽  
Model Parameters ◽  
Longitudinal Assessment ◽  
Union Commitment ◽  
Union Participation

Summary: Previous research has identified instrumentality and ideology as important aspects of member attachment to labor unions. The present study evaluated the construct validity of a scale designed to reflect the two dimensions of instrumental and ideological union commitment using a sample of 1170 Swedish blue-collar union members. Longitudinal data were used to test seven propositions referring to the dimensionality, internal consistency reliability, and temporal stability of the scale as well as postulated group differences in union participation to which the scale should be sensitive. Support for the hypothesized factor structure of the scale and for adequate reliabilities of the dimensions was obtained and was also replicated 18 months later. Tests for equality of measurement model parameters and test-retest correlations indicated support for the temporal stability of the scale. In addition, the results were consistent with most of the predicted differences between groups characterized by different patterns of change/stability in union participation status. The study provides strong support for the construct validity of the scale and indicates that it can be used in future theory testing on instrumental and ideological union commitment.

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