#  Sean Owen
• Male
• Austin, TX
• United States # Sean Owen's Page

## Latest Activity "Conjugates are optional only if they exist or (fixed dimensional) sufficient statistics exist for the likelihood function p(X|θ). If non-informative priors are only left then consider using Jeffreys, reference, probability matching,…"
Mar 11 "Conjugates are optional only if they exist or (fixed dimensional) sufficient statistics exist for the likelihood function p(X|θ). If non-informative priors are only left then consider using Jeffreys, reference, probability matching,…"
Mar 11 "Conjugates are optional only if they exist or (fixed dimensional) sufficient statistics exist for the likelihood function p(X|&theta). If non-informative priors are only left then consider using Jeffreys, reference, probability…"
Mar 11 "Conjugates are optional only if they exist or (fixed dimensional) sufficient statistics exist for the likelihood function p(X|&theta. If non-informative priors are only left then consider using Jeffreys, reference, probability…"
Mar 11 "Conjugates are optional only if they exist or (fixed dimensional) sufficient statistics exist for the likelihood function p(X|&theta) If non-informative priors are only left then consider using Jeffreys, reference, probability…"
Mar 11 "Conjugates are optional only if they exist or (fixed dimensional) sufficient statistics exist for the likelihood function $p(X\theta$. If non-informative priors are only left then consider using Jeffreys, reference, probability…"
Mar 11 "Great post Sean! I don't know where in the post where the approach taken by a frequentist and a Bayesian became clearer to me but wherever it was, thank you.  I am looking forward to your next post on this topic."
Feb 18 Sean Owen posted a blog post

### An Introduction to Bayesian Reasoning

An Introduction to Bayesian ReasoningYou might be using Bayesian techniques in your data science without knowing it! And if you're not, then it could enhance the power of your analysis. This blog post, part 1 of 2, will demonstrate how Bayesians employ probability distributions to add information when fitting models, and reason about uncertainty of the model's fit.Grab a coin. How fair is the coin? What is the probability p that it will land 'heads' when flipped? You flip the coin 5 times and…See More
Feb 15 "I remember long ago when working on my PhD, I was using what was called "penalized likelihood" functions. This was just Bayesian stats in disguise, the "penalty" playing the role of a prior in Bayesian theory."
Feb 14 Sean Owen posted a blog post

### An Introduction to Bayesian Reasoning

An Introduction to Bayesian ReasoningYou might be using Bayesian techniques in your data science without knowing it! And if you're not, then it could enhance the power of your analysis. This blog post, part 1 of 2, will demonstrate how Bayesians employ probability distributions to add information when fitting models, and reason about uncertainty of the model's fit.Grab a coin. How fair is the coin? What is the probability p that it will land 'heads' when flipped? You flip the coin 5 times and…See More
Feb 14 Sean Owen's blog post was featured

### An Introduction to Bayesian Reasoning

An Introduction to Bayesian ReasoningYou might be using Bayesian techniques in your data science without knowing it! And if you're not, then it could enhance the power of your analysis. This blog post, part 1 of 2, will demonstrate how Bayesians employ probability distributions to add information when fitting models, and reason about uncertainty of the model's fit.Grab a coin. How fair is the coin? What is the probability p that it will land 'heads' when flipped? You flip the coin 5 times and…See More
Feb 14

## Profile Information

Field of Expertise
Data Science, Machine Learning, Business Analytics
Databricks
Interests:
Contributing

## Sean Owen's Blog

### An Introduction to Bayesian Reasoning

Posted on February 13, 2019 at 8:00am

An Introduction to Bayesian Reasoning

You might be using Bayesian techniques in your data science without knowing it! And if you're not, then it could enhance the power of your analysis. This blog post, part 1 of 2, will demonstrate how Bayesians employ probability distributions to add information when fitting models, and reason about uncertainty of the model's fit.

Grab a coin. How fair is the coin? What is the probability…

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