bayesian linear regression
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NeuroImage ◽  
2021 ◽  
Vol 245 ◽  
pp. 118715
Author(s):  
Charlotte J. Fraza ◽  
Richard Dinga ◽  
Christian F. Beckmann ◽  
Andre F. Marquand

2021 ◽  
Author(s):  
soumya banerjee

Bayesian models are very important in modern data science. These models can be used to derive estimatesfor noisy and sparse data. This manuscript outlines the basics and derivations of a Bayesian linearregression model. Source code for performing Bayesian linear regression is also provided. I hope thisresource will enable broader understanding of the basics of Bayesian models.


Author(s):  
Olawale Basheer Akanbi

The relationship between government expenditure and its revenue is generating serious debate among researchers. Similarly, their has been a controversy between the classical and the bayesian modelling. Therfore, this study examined the relationship between the government expenditure and its revenue in Nigeria using the bayesian approach. The finance data extracted from the Central Bank of Nigeria statistical bulletin from 1989 to 2018 were considered for the study. Bayesian linear regression was used to fit the model. Normal distribution was fit for the likelihood. Thus, normal-gamma prior was elicited for the bayesian regression parameters. The result showed that the Bayesian estimates with elicited normal-gamma prior produced a better posterior mean of 0.536 for the Total Revenue with a smaller posterior standard deviation of 0.00001 when compared with the OLS standard deviation of 0.05256. Similarly, the total revenue explained 78% variations in the Total expenditure. The constructed model fit was: Total Expenditure = 98.57128 + 0.53630* Total Revenue. This showed that a naira unit of the total expenditure will always be increased by 0.54 of the total revenue. Forecast of 30 years for the total expenditure using both OLS and Bayesian (normal gamma prior) were increasing as the years were progressing. Government should look for a way to increase its revenue in order to sustain the future expenses of the government since expenditure increases yearly.


2021 ◽  
Author(s):  
Amin Shoari Nejad ◽  
Andrew C. Parnell ◽  
Alice Greene ◽  
Peter Thorne ◽  
Brian P. Kelleher ◽  
...  

Abstract. We provide an updated sea level dataset for Dublin for the period 1938 to 2016 at yearly resolution. Using a newly collated sea level record for Dublin Port, as well as two nearby tide gauges at Arklow and Howth Harbour, we perform data quality checks and calibration of the Dublin Port record by adjusting the biased high water level measurements that affect the overall calculation of mean sea level (MSL). To correct these MSL values, we use a novel Bayesian linear regression that includes the Mean Low Water values as a predictor in the model. We validate the re-created MSL dataset and show its consistency with other nearby tide gauge datasets. Using our new corrected dataset, we estimate a rate of 1.08 mm/yr sea level rise at Dublin Port between 1953–2016 (95 % CI from 0.62 to 1.55 mm/yr), and a rate of 6.48 mm/yr between 1997–2016 (95 % CI 4.22 to 8.80 mm/yr). Overall sea level rise is in line with expected trends but large multidecadal varaibility has led to higher rates of rise in recent years.


2021 ◽  
Author(s):  
Victor Agboli

This study investigates the impact of unemployment on the Gross Domestic Product (GDP) of Nigeria for a period of 28 years (1990-2018). The study focuses on the relationship between unemployment and economic growth in Nigeria (GDP). The method used in this study is the Bayesian Linear Regression Analysis, the major findings were that unemployment has a positive impact on the economic growth of Nigeria. Some suggestions and policy recommendations were made based on the findings.


Energies ◽  
2021 ◽  
Vol 14 (17) ◽  
pp. 5556
Author(s):  
Benedetto Grillone ◽  
Gerard Mor ◽  
Stoyan Danov ◽  
Jordi Cipriano ◽  
Florencia Lazzari ◽  
...  

Interpretable and scalable data-driven methodologies providing high granularity baseline predictions of energy use in buildings are essential for the accurate measurement and verification of energy renovation projects and have the potential of unlocking considerable investments in energy efficiency worldwide. Bayesian methodologies have been demonstrated to hold great potential for energy baseline modelling, by providing richer and more valuable information using intuitive mathematics. This paper proposes a Bayesian linear regression methodology for hourly baseline energy consumption predictions in commercial buildings. The methodology also enables a detailed characterization of the analyzed buildings through the detection of typical electricity usage profiles and the estimation of the weather dependence. The effects of different Bayesian model specifications were tested, including the use of different prior distributions, predictor variables, posterior estimation techniques, and the implementation of multilevel regression. The approach was tested on an open dataset containing two years of electricity meter readings at an hourly frequency for 1578 non-residential buildings. The best performing model specifications were identified, among the ones tested. The results show that the methodology developed is able to provide accurate high granularity baseline predictions, while also being intuitive and explainable. The building consumption characterization provides actionable information that can be used by energy managers to improve the performance of the analyzed facilities.


PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0256620
Author(s):  
Sugiarto Sugiarto ◽  
Fadhlullah Apriandy ◽  
Yusria Darma ◽  
Sofyan M. Saleh ◽  
Muhammad Rusdi ◽  
...  

Pretimed signalized intersection is known as a common source of congestion, especially in urban heterogeneous traffic. Furthermore, the accuracy of saturation flow rate is found to cause efficient and vital capacity estimation, in order to ensure optimal design and operation of the signal timings. Presently, the traffic also consists of diverse vehicle presence, each with its own static and dynamic characteristics. The passenger car equivalent (PCE) in an essential unit is also used to measure heterogenous traffic into the PCU (Passenger Car Unit). Based on the collection of observed data at three targets in Banda Aceh City, this study aims to redetermine the PCEs by using Bayesian linear regression, through the Random-walk Metropolis-Hastings and Gibbs sampling. The result showed that the obtained PCE values were 0.24, 1.0, and 0.80 for motorcycle (MC), passenger car (PC), and motorized rickshaw (MR), respectively. It also showed that a significant deviation was found between new and IHCM PCEs, as the source of error was partially due to the vehicle compositions. The present traffic characteristics were also substantially different from the prevailing conditions of IHCM 1997. Therefore, the proposed PCEs enhanced the accuracy of base saturation flow prediction, provided support for traffic operation design, alleviated congestion, and reduced delay within the city, which in turn improved the estimation of signalized intersection capacity.


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Tien Ha My Duong ◽  
Thi Anh Nhu Nguyen ◽  
Van Diep Nguyen

PurposeThe paper aims to examine the impact of social capital on the size of the shadow economy in the BIRCS countries over the period 1995–2014.Design/methodology/approachThe authors employ the Bayesian linear regression method to uncover the relationship between social capital and the shadow economy. The method applies a normal distribution for the prior probability distribution while the posterior distribution is determined using the Markov chain Monte Carlo technique.FindingsThe results indicate that the unemployment rate and tax burden positively affect the size of the shadow economy. By contrast, corruption control and trade openness are negatively associated with the development of this informal sector. Moreover, the paper's primary finding is that social capital represented by social trust and tax morale can hinder the size of the shadow economy.Research limitations/implicationsThis study is limited to the case of the BRICS countries for the period 1995–2014. The determinants of the shadow economy in different groups of countries can be heterogeneous. Moreover, social capital is a multidimensional concept that may consist of various components. This difficulty of measuring the social capital calls for further research on the relationship between other dimensions of social capital and the shadow economy.Originality/valueMany studies investigate the effect of economic factors on the size of the shadow economy. This paper applies a new approach to discover the issue. Notably, the authors use the Bayesian linear regression method to analyze the relationship between social capital and the shadow economy in the BRICS countries.


Author(s):  
Albert Kim ◽  
David Allen ◽  
Simon Couch

1. Neighborhood competition models are powerful tools to measure the effect of interspecific competition. Statistical methods to ease the application of these models are currently lacking. 2. We present the forestecology package providing methods to i) specify neighborhood competition models, ii) evaluate the effect of competitor species identity using permutation tests, and iii) measure model performance using spatial cross-validation. Following Allen (2020), we implement a Bayesian linear regression neighborhood competition model. 3. We demonstrate the package’s functionality using data from the Smithsonian Conservation Biology Institute’s large forest dynamics plot, part of the ForestGEO global network of research sites. Given ForestGEO’s data collection protocols and data formatting standards, the package was designed with cross-site compatibility in mind. We highlight the importance of spatial cross-validation when interpreting model results. 4. The package features i) tidyverse-like structure whereby verb-named functions can be modularly “piped” in sequence, ii) functions with standardized inputs/outputs of simple features ‘sf‘ package class, and iii) an S3 object-oriented implementation of the Bayesian linear regression model. These three facts allow for clear articulation of all the steps in the sequence of analysis and easy wrangling and visualization of the geospatial data. Furthermore, while the package only has Bayesian linear regression implemented, the package was designed with extensibility to other methods in mind.


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