A framework to implement Bayesian model selection and model averaging scheme to linear regression models. It can be used to identify the best model amongst several competing linear regression models.

For the predictive analytics of coronavirus spread, we used a logistic curve model. The data for the analysis were taken from Kaggle competition COVID19 Global Forecasting (Week 2)'. Bayesian ...

1 Describe the fundamental differences between the Bayesian and frequentist approaches to statistics. 2 Construct conjugate families of prior distributions for common sampling distributions. 3 Explain ...

Abstract: Bayesian model selection is a tool for deciding whether the introduction of a new parameter is warranted by the data. I argue that the usual sampling statistic significance tests for a null ...

Abstract: This chapter contains sections titled: Introduction, On Bayesian Networks and Logic Programs, Bayesian Logic Programs, Extensions of the Basic Framework, Learning Bayesian Logic Programs, ...

We study the perfect Bayesian equilibrium of a model of learning over a general social network. Each individual receives a signal about the underlying state of the world, observes the past actions of ...

The three-day international conference on Recent Developments in the Techniques of Bayesian Paradigm concluded at Banaras Hindu University. Sessions featured talks by renowned experts and a ...

Bayesian models are powerful tools for data science, but they also require careful tuning of parameters, such as the number of iterations. Iterations are the steps that the model takes to update ...