scholarly journals Inferring cellular regulatory networks with Bayesian model averaging for linear regression (BMALR)

2014 ◽  
Vol 10 (8) ◽  
pp. 2023-2030 ◽  
Author(s):  
Xun Huang ◽  
Zhike Zi
Keyword(s):  
Linear Regression ◽  
Molecular Interactions ◽  
Computational Efficiency ◽  
Bayesian Model ◽  
Prediction Accuracy ◽  
Regulatory Networks ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
High Prediction ◽  
High Computational Efficiency

A new method that uses Bayesian model averaging for linear regression to infer molecular interactions in biological systems with high prediction accuracy and high computational efficiency.

scholarly journals A tutorial on Bayesian multi-model linear regression with BAS and JASP

2021 ◽  
Author(s):  
Don van den Bergh ◽  
Merlise A. Clyde ◽  
Akash R. Komarlu Narendra Gupta ◽  
Tim de Jong ◽  
Quentin F. Gronau ◽  
...  
Keyword(s):  
Linear Regression ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Theoretical Background ◽  
Theory And Practice ◽  
Parameter Estimates ◽  
Data Set ◽  
Second Stage ◽  
Selection Of

AbstractLinear regression analyses commonly involve two consecutive stages of statistical inquiry. In the first stage, a single ‘best’ model is defined by a specific selection of relevant predictors; in the second stage, the regression coefficients of the winning model are used for prediction and for inference concerning the importance of the predictors. However, such second-stage inference ignores the model uncertainty from the first stage, resulting in overconfident parameter estimates that generalize poorly. These drawbacks can be overcome by model averaging, a technique that retains all models for inference, weighting each model’s contribution by its posterior probability. Although conceptually straightforward, model averaging is rarely used in applied research, possibly due to the lack of easily accessible software. To bridge the gap between theory and practice, we provide a tutorial on linear regression using Bayesian model averaging in , based on the BAS package in . Firstly, we provide theoretical background on linear regression, Bayesian inference, and Bayesian model averaging. Secondly, we demonstrate the method on an example data set from the World Happiness Report. Lastly, we discuss limitations of model averaging and directions for dealing with violations of model assumptions.

scholarly journals A Tutorial on Bayesian Multi-Model Linear Regression with BAS and JASP

2020 ◽  
Author(s):  
Don van den Bergh ◽  
Merlise Aycock Clyde ◽  
Akash Raj ◽  
Tim de Jong ◽  
Quentin Frederik Gronau ◽  
...  
Keyword(s):  
Linear Regression ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Theoretical Background ◽  
Theory And Practice ◽  
Parameter Estimates ◽  
Data Set ◽  
Second Stage ◽  
Selection Of

Linear regression analyses commonly involve two consecutive stages of statistical inquiry. In the first stage, a single ‘best’ model is defined by a specific selection of relevant predictors; in the second stage, the regression coefficients of the winning model are used for prediction and for inference concerning the importance of the predictors. However, such second-stage inference ignores the model uncertainty from the first stage, resulting in overconfident parameter estimates that generalize poorly. These drawbacks can be overcome by model averaging, a technique that retains all models for inference, weighting each model’s contribution by its posterior probability. Although conceptually straightforward, model averaging is rarely used in applied research, possibly due to the lack of easily accessible software. To bridge the gap between theory and practice, we provide a tutorial on linear regression using Bayesian model averaging in JASP, based on the BAS package in R. Firstly, we provide theoretical background on linear regression, Bayesian inference, and Bayesian model averaging. Secondly, we demonstrate the method on an example data set from the World Happiness Report. Lastly, we discuss limitations of model averaging and directions for dealing with violations of model assumptions.

scholarly journals Bayesian Model Averaging Using Power-Expected-Posterior Priors

Econometrics ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 17
Author(s):  
Dimitris Fouskakis ◽  
Ioannis Ntzoufras
Keyword(s):  
Bayesian Model ◽  
Prediction Accuracy ◽  
Bayesian Model Averaging ◽  
Linear Models ◽  
Model Averaging ◽  
Real Data ◽  
Bayesian Variable Selection ◽  
Point Estimate ◽  
Model Average ◽  
Evaluation Strategies

This paper focuses on the Bayesian model average (BMA) using the power–expected– posterior prior in objective Bayesian variable selection under normal linear models. We derive a BMA point estimate of a predicted value, and present computation and evaluation strategies of the prediction accuracy. We compare the performance of our method with that of similar approaches in a simulated and a real data example from economics.

Selecting Predictors by Using Bayesian Model Averaging in Bridge Models

2012 ◽  
Author(s):  
Lorenzo Bencivelli ◽  
Massimiliano Giuseppe Marcellino ◽  
Gianluca Moretti
Keyword(s):  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Bridge Models
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