Linked Questions
19 questions linked to/from Bayesian updating with new data
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Bayesian inference: all at once vs one at a time [duplicate]
Suppose that I have a prior on a parameter $\theta$ and update this prior in light of the realisation of $n$ random variables. It seems plausible that it is equivalent to update the prior $n$ times, ...
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Calculate posterior from normal prior and some observations [duplicate]
I know Bayes Theorem in a basic way. If I was given prior and likelihood in the form of probabilities I can fill in the formula. When watching this video the following graph was discussed (2:50):
The ...
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85
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2
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46k
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Bayes regression: how is it done in comparison to standard regression?
I got some questions about the Bayesian regression:
Given a standard regression as $y = \beta_0 + \beta_1 x + \varepsilon$.
If I want to change this into a Bayesian regression, do I need prior ...
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46
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3
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12k
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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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5
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2k
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Does Bayesian statistics make meta-analysis obsolete?
I'm just wondering if Bayesian statistics would be applied consequently from the first study to the last if this makes a meta-analysis obsolete.
For example, let's assume 20 studies which have been ...
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8
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1
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3k
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Taking into account the uncertainty of p when estimating the mean of a binomial distribution
I have a binomial distribution with parameters $N$ and $p$, and the estimate for the mean of my distribution is N$\times p$. The values of $N$ and $p$ are such that we can use the Gaussian ...
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3
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339
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Finding conditional expectation of conditional distribution
Let $a \sim N(\mu_a,1/\tau)$, and $s = a + \epsilon$, where $\epsilon \sim N(0,1/\eta)$. I know that because both $a$ and $\epsilon$ is normal distribution, s must also be normally distributed with $s ...
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1
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Bayesian updating with conjugate priors using the closed form expressions
I have one two data sets of scalar values: one large data set (about 700 data points) and one small data set (80 data points). I would like to update the large data set with the small one using the ...
- 133
-2
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2
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3k
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What are "prior distribution" and "posterior distribution" in the case of Bayesian statistics? [closed]
What are "prior distribution" and "posterior distribution" in the case of Bayesian statistics?
Can you give layman's examples?
I understand prior and posterior probabilities. ...
- 2,136
1
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1
answer
3k
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What does "Normal Distribution is conjugate to Normal" mean? [duplicate]
I kind of understand the meaning of conjugate to something but can't really get the clear picture of the concept. Some video lectures on youtube talk about it but they all seem to just say "Normal ...
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Calculating the parameters of a Normal distribution using alpha and beta from Inverse-gamma (conjugate prior)
How is it possible to calculate the variance $\sigma^2$ for the Normal distribution if only $\alpha$ and $\beta$ (based on data) from the Inverse-gamma distribution are available? I followed the ...
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1
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823
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Combining the result of two uncertain measurements
What is the best way to combine the results of multiple uncertain measurements?
For example, let us assume that I want to measure the relation y ~ b*x. I run my experiment and I estimate the the ...
1
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3
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441
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Practically, how to update a Bayesian distribution for a single parameter
First of all let me say that I have looked at this very helpful answer, but I don't believe it fully answers my question.
Background
Let's say I have a single parameter $\theta$ that I want to know ...
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1
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0
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274
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Bayesian learning - how to update an inverse gamma distribution
I'm trying to implement a Bayesian Learning/Updating Model (multi-armed bandit) in the following way:
I'm conducting a survey where respondents can rate items on a 5-point scale.
I have a total set ...
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2
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71
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Is it wrong to estimate its moment probability with Bayes, beta parameter
As far as I know I can decide beta parameter for Bayes estimation.
Let’s say we estimate probability of coin flip distribution, and choose Uniform Distribution as Beta(1,1)
If 9 of 10 flips will come ...
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