Linked Questions
66 questions linked to/from What is an "uninformative prior"? Can we ever have one with truly no information?
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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?
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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 ...
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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 ...
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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 ...
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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 ...
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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♦
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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 ...
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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. ...
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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 ...
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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 ...
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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(...
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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 ...
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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 "...
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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 ...
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Why Aren't "Non-Informative Priors" More Popular?
Recently, I have been reading about how Confidence Intervals in the Frequentist setting are often misinterpreted (e.g. The importance of a correct interpretation of a confidence interval).
Supposedly, ...
