How to Get Started with Bayesian Statistics

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Discussion Overview

The discussion revolves around getting started with Bayesian statistics, particularly from the perspective of non-statisticians. Participants share their experiences, challenges, and insights related to understanding and applying Bayesian methods in various contexts, including biomedical research and statistical analysis.

Discussion Character

  • Exploratory
  • Technical explanation
  • Conceptual clarification
  • Debate/contested
  • Mathematical reasoning

Main Points Raised

  • One participant expresses their background in biomedical engineering and their interest in Bayesian statistics, noting its application in their research.
  • Another participant shares their struggle with grasping the deeper meaning of Bayesian thinking despite reading about it.
  • A participant mentions plans to write more articles on Bayesian probability and its scientific applications, acknowledging the initial difficulty of the topic.
  • One participant reflects on their undergraduate course in Bayesian models, stating they learned little and expressing appreciation for the explanations provided in the thread.
  • A humorous exchange occurs regarding testing knowledge through questions about prior and posterior distributions.
  • Technical issues are discussed regarding accessing links to resources, with participants noting problems related to data allotments and software installations.
  • A participant shares their recent analyses comparing informed and default priors, questioning why the effect sizes were smaller and credible intervals narrower with the informed prior.

Areas of Agreement / Disagreement

Participants generally express a shared interest in Bayesian statistics and acknowledge its complexities. However, there is no consensus on specific interpretations or methodologies, and several questions remain unresolved.

Contextual Notes

Some discussions involve personal experiences with Bayesian statistics education and practical applications, highlighting limitations in understanding and the challenges of using different priors in analyses.

Who May Find This Useful

This discussion may be useful for individuals interested in Bayesian statistics, particularly those from non-statistical backgrounds seeking to understand its applications and challenges.

Dale
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Confessions of a moderate Bayesian, part 1
Bayesian statistics by and for non-statisticians
https://www.cafepress.com/physicsforums.13265286
Background
I am a statistics enthusiast, although I am not a statistician. My training is in biomedical engineering, and I have been heavily involved in the research and development of novel medical imaging technologies for the bulk of my career. Due to my enjoyment of statistics and my professional research needs I was usually the author in charge of the statistical analysis of most of our papers.
In the course of that work, I developed an interest in and an appreciation of Bayesian statistics, which I have used in the analysis of most of my more recent projects. I have published the Bayesian analysis of some of these studies, although more were done in addition to standard (published) frequentist analysis.
This article will be a quick and practical how-to on getting started doing...

Continue reading...
 
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Nice article, @Dale!

Bayesian thinking has also been interesting and confusing to me. I read about it and understand it until I try to explain it and then I just don't have the proper intuition to understand its deeper meaning.
 
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I plan on following up with some more articles that will deal with Bayesian probability itself and the use of Bayesian statistics in science. I agree that it can be a little hard at first too.
 
Although I took a course as an undergraduate ostensibly covering Bayesian models, I realize I learned approximately nothing! (I believe it was in the psych dept...part of rounding out my bachelor of arts)
That being said I have learned much from your various explanations and look forward to the article(s). Thanks.
 
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hutchphd said:
That being said I have learned much from your various explanations and look forward to the article(s).
I appreciate that! Hopefully I can get the next one fleshed out tonight or tomorrow.
 
hutchphd said:
Although I took a course as an undergraduate ostensibly covering Bayesian models, I realize I learned approximately nothing! (I believe it was in the psych dept...part of rounding out my bachelor of arts)
That being said I have learned much from your various explanations and look forward to the article(s). Thanks.
That may just be your prior. Let's ask you a few questions to test your knowledge to get a posterior, like I did when I asked to show full work in an exam, given a Prior: " Show your Posterior" . Won't make that mistake again ;)
 
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WWGD said:
Link points to the PF Shop, not the Insights .
It seems to be working now, but I didn’t do anything to fix it
 
May just be my PC. This is what I am getting:

1606971048548.png
 
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Never mind. I had run out of my monthly data allotment and I get weird stuff like this occasionally.

EDIT: Have you used R within Jupyter? Jupyter stands for : Julya, Python and R. Now too, SQL Server Developer 2017 has both R and a Python Servers.
 
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WWGD said:
Never mind. I had run out of my monthly data allotment and I get weird stuff like this occasionally.

EDIT: Have you used R within Jupyter? Jupyter stands for : Julya, Python and R. Now too, SQL Server Developer 2017 has both R and a Python Servers.
I have not. I tried to get Jupyter set up because it sounded useful to have both Python and R together and I was curious about Julia. But my installation didn't work the first time around so I didn't pursue it further.
 
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Dale said:
Continue reading...
Hi

I recently ran several analyses using the informed prior vs the default prior. I understand why the same analyses using the informed prior gives a bigger Bayes factor compared to the default, but I didn’t expect the effect sizes to all be smaller (and credible intervals narrower) when using the informed prior. Does anybody know why this might be?
 

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