Bayesian inference under log-normality assumption must be performed very carefully. In fact, under the common priors for the variance, useful quantities in the original data scale (like mean and quantiles) do not have posterior moments that are finite (Fabrizi et al. 2012 ). This package allows to easily carry out a proper Bayesian inferential procedure by fixing a suitable distribution (the generalized inverse Gaussian) as prior for the variance. Functions to estimate several kind of means (unconditional, conditional and conditional under a mixed model) and quantiles (unconditional and conditional) are provided
In the Bayesian approach, the data are supplemented with additional information in the form of a pri...
Maximum likelihood and Bayesian estimation are both frequently used to fit mixed logit models to cho...
Random probability measures are the main tool for Bayesian nonparametric inference, with their laws ...
Bayesian inference under log-normality assumption must be performed very carefully. In fact, under t...
The log-normal distribution is very popular for modeling positive right-skewed data and represents a...
The main topic of the thesis is the proper execution of a Bayesian inference if log-normality is ass...
Log-normal linear regression models are popular in many \ufb01elds of research.Bayesian estimation o...
The log-normal distribution is a popular model in biostatistics and other fields of statistics. Baye...
The log-normal distribution is a popular model in biostatistics as in many other fields of statistics...
The analysis of variance, and mixed models in general, are popular tools for analyzing experimental ...
This Python package helps to perform a Bayesian analysis of log-normally distributed data (PYthon pa...
Bayesian inference can be extended to probability distributions defined in terms of their inverse di...
The lognormal distribution is a popular model in many fields of statistics. The mean, the mode, the ...
Krishnamoorthy, Mathewand Ramachandran (2006) developed a method to draw inference on the mean and v...
Bayesian methods of inference are the appropriate statistical tools for providing interval estimates...
In the Bayesian approach, the data are supplemented with additional information in the form of a pri...
Maximum likelihood and Bayesian estimation are both frequently used to fit mixed logit models to cho...
Random probability measures are the main tool for Bayesian nonparametric inference, with their laws ...
Bayesian inference under log-normality assumption must be performed very carefully. In fact, under t...
The log-normal distribution is very popular for modeling positive right-skewed data and represents a...
The main topic of the thesis is the proper execution of a Bayesian inference if log-normality is ass...
Log-normal linear regression models are popular in many \ufb01elds of research.Bayesian estimation o...
The log-normal distribution is a popular model in biostatistics and other fields of statistics. Baye...
The log-normal distribution is a popular model in biostatistics as in many other fields of statistics...
The analysis of variance, and mixed models in general, are popular tools for analyzing experimental ...
This Python package helps to perform a Bayesian analysis of log-normally distributed data (PYthon pa...
Bayesian inference can be extended to probability distributions defined in terms of their inverse di...
The lognormal distribution is a popular model in many fields of statistics. The mean, the mode, the ...
Krishnamoorthy, Mathewand Ramachandran (2006) developed a method to draw inference on the mean and v...
Bayesian methods of inference are the appropriate statistical tools for providing interval estimates...
In the Bayesian approach, the data are supplemented with additional information in the form of a pri...
Maximum likelihood and Bayesian estimation are both frequently used to fit mixed logit models to cho...
Random probability measures are the main tool for Bayesian nonparametric inference, with their laws ...