Linked Questions

0 votes
1 answer
1k views

What is the importance of non-informative prior in Bayesian Inference? [duplicate]

By the name, noninformative prior, the prior distribution doesn't contain any information about the parameter. Then why we use this thing to estimate the parameter by the Bayesian approach?
Prakash Chandra's user avatar
0 votes
1 answer
583 views

Uninformative (flat) Prior density for non-linear functions [duplicate]

We, bayesians, usually use non-informative priors for the parameters like $p(\beta)\propto 1$. Someone told me that such a flat prior is informative to some extent for non-linear functions of the ...
adrian1121's user avatar
  • 1,116
3 votes
1 answer
111 views

Choosing Bayesian Priors [duplicate]

I am fairly new to Bayesian Modeling, however I am experimenting with such framework in order to produce several estimates. The part I am struggling the most with is the selection of prior ...
Marco De Virgilis's user avatar
1 vote
0 answers
144 views

Information about parameters using priors distributions [duplicate]

When using the "non-informative" prior $\pi(\mu,\sigma)\propto\frac{1}{\sigma^2}$ where $\pi(\mu)\propto1$ and $\pi(\sigma^2)\propto\frac{1}{\sigma^2}$ Where is the no information for the ...
user208618's user avatar
0 votes
0 answers
34 views

Are there any uninformative priors with an unlimited support like $(-\infty,\infty), (0,\infty), (-\infty,0)$? [duplicate]

The Bayes theorem is: $P(\theta | x)=\displaystyle \frac{p(\theta)L_x(\theta)}{\int_{\theta \in A}p(\theta)L_x(\theta)d\theta}$ It's pretty clear that $\theta's$ support will not change as bayes ...
Davi Américo's user avatar
0 votes
0 answers
18 views

In what noninformative priors turn out to be informative? [duplicate]

When searching about noninformative priors on internet, one can read here and there that those priors in fact turn out to be informative. However, I did not yet read a real argument about that. So my ...
Celi's user avatar
  • 1
90 votes
15 answers
24k views

When (if ever) is a frequentist approach substantively better than a Bayesian?

Background: I do not have an formal training in Bayesian statistics (though I am very interested in learning more), but I know enough--I think--to get the gist of why many feel as though they are ...
116 votes
10 answers
8k views

ASA discusses limitations of $p$-values - what are the alternatives?

We already have multiple threads tagged as p-values that reveal lots of misunderstandings about them. Ten months ago we had a thread about psychological journal that "banned" $p$-values, now American ...
Tim's user avatar
  • 140k
155 votes
3 answers
249k views

Help me understand Bayesian prior and posterior distributions

In a group of students, there are 2 out of 18 that are left-handed. Find the posterior distribution of left-handed students in the population assuming uninformative prior. Summarize the results. ...
Bob's user avatar
  • 1,551
46 votes
3 answers
13k views

Do Bayesian priors become irrelevant with large sample size?

When performing Bayesian inference, we operate by maximizing our likelihood function in combination with the priors we have about the parameters. Because the log-likelihood is more convenient, we ...
pixels's user avatar
  • 649
21 votes
1 answer
16k views

Choosing between uninformative beta priors

I am looking for uninformative priors for beta distribution to work with a binomial process (Hit/Miss). At first I thought about using $\alpha=1, \beta=1$ that generate an uniform PDF, or Jeffrey ...
Mateus's user avatar
  • 211
32 votes
4 answers
2k views

History of uninformative prior theory

I am writing a short theoretical essay for a Bayesian Statistics course (in an Economics M.Sc.) on uninformative priors and I am trying to understand which are the steps in the development of this ...
PhDing's user avatar
  • 3,099
21 votes
4 answers
4k views

How is the bayesian framework better in interpretation when we usually use uninformative or subjective priors?

It is often argued that the bayesian framework has a big advantage in interpretation (over frequentist), because it computes the probability of a parameter given the data - $p(\theta|x)$ instead of $p(...
Tomas's user avatar
  • 6,211
23 votes
4 answers
5k views

How do Bayesian Statistics handle the absence of priors?

This question was inspired by two recent interactions I had, one here in CV, the other over at economics.se. There, I had posted an answer to the well-known "Envelope Paradox" (mind you, not as the "...
Alecos Papadopoulos's user avatar
12 votes
4 answers
4k views

Why I should use Bayesian inference with uninformative prior? [duplicate]

I am a Ph.D. student and currently I am studying Bayesian inference concerning vector autoregressive models. A lot of researchers when talking about uninformative prior, conclude that the results of ...
Mario's user avatar
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