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  • Why I should use Bayesian inference with uninformative prior?
    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 inference are equal to what we can obtain using OLS My question is: if this is true, why I should use Bayesian inference instead of OLS?
  • What is an uninformative prior? Can we ever have one with truly no . . .
    In many models it is possible to set an "uninformative" set of priors that allows some moments (e g , the prior mean) to vary over the entire possible range of values, and this nonetheless produces valuable posterior results, where the posterior moments are bounded more tightly
  • Choosing between uninformative beta priors - Cross Validated
    Below you can see posterior distributions resulting from five different "uninformative" priors (described below the plot) given different data As you can clearly see, the choice of "uninformative" priors affected the posterior distribution, especially in cases where the data itself did not provide much information
  • bayesian - History of uninformative prior theory - Cross Validated
    A few comments about flaws of noninformative priors (uninformative priors) are probably a good idea since the investigation of such flaws helped development of the concept of noninformative prior in history You may want to add some comments about the drawbacks flaws of adopting noninformative priors Among many criticisms I point out two
  • Why are Jeffreys priors considered noninformative?
    The first is to reject the notion of uninformative priors altogether because the Bayesian posterior is still different from the frequentist likelihood The second is to say that by using the Jeffreys prior we finally have a method under which all values of $\theta$ are equally likely before we have seen the data (under frequentist likelihood
  • Why a truly uninformative prior does not exist? [duplicate]
    $\begingroup$ The 2nd paragraph of Xian's answer in the related thread basically says that 'uninformative' in a sense of maximum entropy will still require constraints for the distribution whose entropy is maximises, and also the base measure playes an influence (in a similar way as the uniform prior elbeing related tonthe base measure)
  • Prior comparison: Uninformative vs informative - Cross Validated
    The purpose of the prior is to include real information that you have about the likely location of the parameter As an example, imagine that you had performed prior research on the topic and so you used those parameter estimates as your prior As a comparison, you used an uninformative prior to show the difference
  • What is the bayesian uninformative exponential prior?
    If it is not possible to do so in an uninformative way, I would also like to know if there are methods to sensibly pick $\alpha$ or possibly whether there is a more suitable prior distribution The output would be a distribution around $\lambda$
  • Understanding definition of informative and uninformative prior . . .
    The term "uninformative prior" is somewhat of a misnomer Such a prior might also be called a not very informative prior, or an objective prior, i e one that's not subjectively elicited Uninformative priors can express "objective" $\color{blue}{\text{information}}$ such as "the variable is positive" or "the variable is less than some limit"





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