Posterior Density vs. Posterior Distribution

In summary, posterior density and posterior distribution are often used interchangeably, but usually the term distribution is used to describe the type of distribution, such as normal or gamma. The equation given for posterior distribution appears to actually be a posterior density function for a continuous random variable. The difference between the two terms is that distribution refers to the overall shape of the data, while density refers to the specific value at a given point. When finding the mode of a posterior distribution, only the numerator needs to be considered because the denominator is a constant and does not affect the location of the maximum point.
  • #1
gajohnson
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0

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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  • #2
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.
 
  • #3
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!
 

FAQ: Posterior Density vs. Posterior Distribution

What is the difference between posterior density and posterior distribution?

Posterior density and posterior distribution are two ways of representing the same concept in Bayesian statistics. The main difference is that posterior density is a continuous function, while posterior distribution is a discrete set of values. Posterior density is often used when the data is continuous, while posterior distribution is used when the data is discrete.

How is posterior density calculated?

Posterior density is calculated using Bayes' theorem, which combines prior knowledge about a parameter with observed data to update our beliefs about the parameter. It involves multiplying the prior density function by the likelihood function, and then normalizing the result to ensure it integrates to 1.

What is the interpretation of posterior density?

Posterior density represents the probability distribution of a parameter after taking into account both prior knowledge and observed data. It shows the likelihood of different values for the parameter being true, based on the available information.

How is posterior distribution different from prior distribution?

Prior distribution represents our beliefs about a parameter before any data is observed, while posterior distribution represents our beliefs about the parameter after incorporating observed data. In other words, posterior distribution is an updated version of the prior distribution.

Can posterior density and posterior distribution be used interchangeably?

While both posterior density and posterior distribution represent the same concept in Bayesian statistics, they cannot be used interchangeably. This is because they have different mathematical properties and are used in different scenarios. Posterior density is used when the data is continuous, while posterior distribution is used when the data is discrete.

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