Posterior Density vs. Posterior Distribution

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SUMMARY

The discussion clarifies the distinction between posterior density and posterior distribution in Bayesian statistics. The posterior distribution is defined mathematically as the product of the likelihood and prior density divided by the integral of the likelihood times prior density. While the terms are sometimes used interchangeably, "density" typically refers to a function for continuous random variables, whereas "distribution" describes a type, such as normal or gamma distribution. The conversation also touches on the concept of the mode of a posterior distribution, emphasizing that only the numerator needs to be maximized when finding the Maximum A Posteriori (MAP) estimate.

PREREQUISITES
  • Understanding of Bayesian statistics
  • Familiarity with likelihood and prior density concepts
  • Knowledge of continuous random variables
  • Basic grasp of probability distributions
NEXT STEPS
  • Study the mathematical formulation of posterior distribution in Bayesian inference
  • Learn about Maximum A Posteriori (MAP) estimation techniques
  • Explore different types of probability distributions, such as normal and gamma distributions
  • Investigate the role of cumulative distribution functions in statistical analysis
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Statisticians, data scientists, and anyone involved in Bayesian analysis or probabilistic modeling will benefit from this discussion.

gajohnson
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Homework Statement



Explain the difference between posterior density and posterior distribution

Homework Equations



NA

The Attempt at a Solution



This isn't a homework question per se, but it will help with something I'm working on. Anyway, my textbook defines posterior distribution as:

Likelihood * Prior Density/ ∫Likelihood X Prior Density

However, it goes on to talk about posterior density without explicitly discussing the differences, and I can't tell if those two terms are interchangeable or not. For instance, one question asks me to find the posterior density of something, and another the posterior distribution. Any help with these concepts would be greatly appreciated!
 
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gajohnson said:

Homework Statement



Explain the difference between posterior density and posterior distribution

Homework Equations



NA

The Attempt at a Solution



This isn't a homework question per se, but it will help with something I'm working on. Anyway, my textbook defines posterior distribution as:

Likelihood * Prior Density/ ∫Likelihood X Prior Density

However, it goes on to talk about posterior density without explicitly discussing the differences, and I can't tell if those two terms are interchangeable or not. For instance, one question asks me to find the posterior density of something, and another the posterior distribution. Any help with these concepts would be greatly appreciated!

Sometimes (not too often) the words "density" and "distribution" are used almost interchangeably although *usually* the word distribution is used more as a descriptor of "type"---as, for example, normal distribution or gamma distribution or Poisson distribution. Nowadays, the term 'distribution function' is being used increasingly in place of the term 'cumulative distribution function'.

The thing you wrote above looks to me like a posterior *density* function, assuming you are speaking of a continuous random variable.
 
Ray Vickson said:
Sometimes (not too often) the words "density" and "distribution" are used almost interchangeably although *usually* the word distribution is used more as a descriptor of "type"---as, for example, normal distribution or gamma distribution or Poisson distribution. Nowadays, the term 'distribution function' is being used increasingly in place of the term 'cumulative distribution function'.

The thing you wrote above looks to me like a posterior *density* function, assuming you are speaking of a continuous random variable.

Thanks! I do get the impression that my book is using them interchangeably here, and I am talking about a continuous random variable. If they weren't being used interchangeably, what would the difference be?

In addition, maybe you can answer another qualitative question for me. In finding the mode of a posterior distribution (the MAP), why do I not need to consider the denominator of the equation that I mentioned earlier? I understand that, in practice, I only need to maximize the numerator, but I'm not exactly sure why. Thank you!
 

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