The approach and interpretation of probability associated with Bayes' theorem; usually used as opposed to the frequentist approach. It can be seen as an extension of logic that enables reasoning with propositions whose truth or falsity is uncertain. A Bayesian probabilist starts with some prior probability, and evaluates the evidence in favour of a hypothesis by combining the prior with the likelihood function of the observed data.
Questions tagged [bayesian]
2030 questions
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Bayesian updating with a prior that combines two normals with different variances
I would like to derive the following and need some help. Thanks in advance.
Suppose a continuous variable $x$ has the following distribution,
$x\sim N^{+}(0,\sigma_L^2)\; if\; x>0$ and $x\sim N^-(0,\sigma_H^2)\; if\; x<0$, where…
allen
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1 answer
Bayesian average with penalty when R approaches 0?
In a system with chunks of arbitrary number (5-200) of questions and quantifiable answers, I'm calculating multiple bayesian average values. One for each one of these chunks of questions. Due to the fuzzy nature of these questions, the result is…
l33t
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1 answer
Bayes with Log-Normal Data
There is some (recent) evidence that neurological activity is log-normally distributed. Does this invalidate the use of Bayes Theorem with these data?
I ask because a major branch of computational neuroscience is based in Bayes, and I am concerned…
johnhidley
- 11
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1 answer
Improper Prior Distribution
What is the clear mathematics definition about improper prior distributions? Can you give me some book or article links about it?
mariovnara
- 71
-4
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1 answer
Can likelihood be changed when the prior changes?
I have a data which follows gamma distribution and want to know the uncertainty of the parameters of this data.
•Data∼Gamma(α,β)
•Parameters
α∼Gamma(kα,θα)
β∼Gamma(kβ,θβ)
I used Winbugs (code below).
model{
for (i in 1:N){
Y[i] ~ dgamma(k,…
moon
- 7
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1 answer
Question on the Bayesian equation for $ p(w|y,x)$
I'm new to this topic.
Suppose that
$ f(x) = x^Tw$, where both $x$ and $w$ are independent random variables with known probability density function.
$ y(x) = f(x) + \epsilon $ where $ \epsilon \sim N(0, \sigma^2_n)$, and $\epsilon$ is independent…
Trainer99
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