Bayesian Statistics

3 votes
1079 reads

Article: Beyond Gaussian Statistical Modeling

It's always good to have a comprehensive and summarized overview of a context. A paper by Bocquet et al. in Monthly Weather Review (from Aug. 2010) has done this about the Data Assimilation methods that go beyond the Gaussian Statistical models. The paper is entitled "Beyond Gaussian Statistical Modeling in Geophysical Data Assimilation".

3 votes
2969 reads

MCMC programming in R, Python, Java and C

Markov Chain Monte Carlo (MCMC) is a powerful simulation technique for exploring posterior distributions that arise in Bayesian Statistics.

4 votes
1464 reads

The Future of Data Analysis

I was reading Andrew Gelman's blog and I saw a very interesting article written by Aleks Jakulin on " The future of Data Analysis ". Although the post is a little old, posted back in 2008, but it's worth-reading. The post mainly explains the benifits of the Bayesian approach in data analysis as compared to frequentist methods.

3 votes
1079 reads

Probabilistic reasoning for assembly-based 3D modeling

Here's an interesting application of a Bayesian network in interactive interfaces from SIGGRAPH 2011.  In this study, model learns how different shapes are conected to each other, and use this knowledge to recommend related components to users during the process of 3D modeling.

for more information go to :

2 votes
1244 reads

The strength of evidence versus the power of belief: Are we all Bayesians? - A talk by Dr. Jessica Utts

"Although statisticians have the job of making conclusions based on data, for many questions in science and society prior beliefs are strong and may take precedence over data when people make decisions. For other questions, there are experts who could shed light on the situation that may not be captured with available data. One of the appealing aspects of Bayesian statistics is that the methods allow prior beliefs and expert knowledge to be incorporated into the analysis along with the data.

5 votes
3029 reads

Bayesian Estimation of Negative Binomial Regression Using SAS

What is Negative Binomial Regression?

If you want to model count variables, negative binomial regression is a good choice. In this regression, the count variable, outcome, is regressed to your covariate of interest. As an example suppose that an insurance company is interested in analyzing the behavior of its insurees on visiting hospitals based on their characteristics to design the most appropriate and cost efficient plans for its customers.

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