scholarly journals vbmp: Variational Bayesian Multinomial Probit Regression for multi-class classification in R

2007 ◽  
Vol 24 (1) ◽  
pp. 135-136 ◽  
Author(s):  
N. Lama ◽  
M. Girolami
Keyword(s):  
Multinomial Probit ◽  
Probit Regression ◽  
Variational Bayesian ◽  
Multi Class Classification ◽  
Multinomial Probit Regression

Cooking fuel choices of households in urban areas in Uganda: a multinomial probit regression analysis

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Joseph Elasu ◽  
Bright Richard Richard ◽  
Muyiwa S. Adaramola
Keyword(s):  
Urban Areas ◽  
Sociodemographic Factors ◽  
Multinomial Probit ◽  
Probit Regression ◽  
Cooking Fuel ◽  
Content Type ◽  
Explanatory Variables ◽  
Section Data ◽  
Making Choices ◽  
Multinomial Probit Regression

PurposeThis study explores the economic and sociodemographic factors that influence households' decisions on the type of fuel used for cooking in urban areas in Uganda.Design/methodology/approachIn total, two cross-section data surveyed by the Uganda Bureau of Statistics (UBOS) in 2012/13 and 2016/17 were used to analyze consumption of energy for cooking purposes in urban areas of Uganda. This paper employed a multinomial probit regression model and the corresponding marginal effects to analyze cooking fuel choices, which are biomass, electricity and gas and kerosene combined.FindingsThe results showed that household expenditure was statistically significant for the choice of cooking fuel chosen. Furthermore, kitchen type, dwelling type and apartment tenure type are found to be significantly influence the choice of household cooking fuel decisions.Originality/valueThis study takes into consideration the combined influence of the kitchen type, dwelling and tenure type as explanatory variables for the choice of cooking fuel for households in urban areas in Uganda. These factors have not been considered in previous studies done in Uganda, especially within the context of urban households when making choices for cooking fuel.

scholarly journals Income Diversification by agro- Pastoralists in Cholistan Desert Punjab, Pakistan

2021 ◽  
Vol 23 (2) ◽  
Author(s):  
Ramsha Saleem ◽  
Ammara Amjad Hashmi ◽  
Hafsah Batool ◽  
Muhammad Naeem
Keyword(s):  
Multinomial Probit ◽  
Probit Regression ◽  
Income Diversification ◽  
Role Of Government ◽  
Livestock Sector ◽  
Unskilled Labour ◽  
Cholistan Desert ◽  
Diversification Strategies ◽  
Multinomial Probit Regression

The pastoralists are economically depend upon livestock for their income which include their herds of livestock and the bi products produced and sold. The nomads keep moving in search of food and forage so they do not completely destroy the natural resource of a particular area. During their journey of searching water sometimes make them closer to the agriculture land near towns where they used to earn through off farm activities which include the income earned through their unskilled labour activities. The multinomial probit regression employed in this study for analysis revealed that many pastoralists adopt the income diversification strategies which are the coping strategies for other than livestock income to reduce the risks attached with livestock income. The role of Govt. and NGOs for improvement in infrastructure is envisaged to find the enhancement of livestock sector in the area is explored in this study. The study is unique in providing perspective on providing access to different facilities and the role of government in improving living of population.

scholarly journals Variational Bayesian Multinomial Probit Regression with Gaussian Process Priors

2006 ◽  
Vol 18 (8) ◽  
pp. 1790-1817 ◽  
Author(s):  
Mark Girolami ◽  
Simon Rogers
Keyword(s):  
Gaussian Process ◽  
Latent Variables ◽  
Data Augmentation ◽  
A Priori ◽  
Covariance Structure ◽  
Variational Approximation ◽  
Probit Regression ◽  
Variational Bayesian ◽  
Augmentation Strategy ◽  
Sparse Approximations

It is well known in the statistics literature that augmenting binary and polychotomous response models with gaussian latent variables enables exact Bayesian analysis via Gibbs sampling from the parameter posterior. By adopting such a data augmentation strategy, dispensing with priors over regression coefficients in favor of gaussian process (GP) priors over functions, and employing variational approximations to the full posterior, we obtain efficient computational methods for GP classification in the multiclass setting.1 The model augmentation with additional latent variables ensures full a posteriori class coupling while retaining the simple a priori independent GP covariance structure from which sparse approximations, such as multiclass informative vector machines (IVM), emerge in a natural and straightforward manner. This is the first time that a fully variational Bayesian treatment for multiclass GP classification has been developed without having to resort to additional explicit approximations to the nongaussian likelihood term. Empirical comparisons with exact analysis use Markov Chain Monte Carlo (MCMC) and Laplace approximations illustrate the utility of the variational approximation as a computationally economic alternative to full MCMC and it is shown to be more accurate than the Laplace approximation.

Variational Bayesian Learning Theory

2019 ◽  
Author(s):  
Shinichi Nakajima ◽  
Kazuho Watanabe ◽  
Masashi Sugiyama
Keyword(s):  
Learning Theory ◽  
Bayesian Learning ◽  
Variational Bayesian
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