# Tagged Questions

Refers to the probability distribution of parameters conditioned on data in Bayesian statistics.

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### measuring errors of bias, dispersion and outlier rate

I fit different models to a sample of data using Bayesian statistics. I have obtained for each data point in the sample a posterior probability distribution. Assuming I know the true answers for the ...
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### Quantify compatibility between posterior estimates

I performed two distinct, independent experiment $E_1$ and $E_2$ to ideally measure the same quantity $X \in \mathbb{R}$ of interest. For each experiment, I computed the posterior pdf of $X$ via MCMC, ...
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### Posterior distribution under Cauchy prior?

I have a (I hope) simple question! If I had a linear regression, $Y_t = \alpha + \beta X_t + \epsilon_t$ with $\epsilon_t \sim N(0,\sigma^2)$ and I assume a Cauchy prior for $\sigma$, is it ...
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### What are the hyperparameters? [duplicate]

I find the meaning of hyperparameters not always clear. The hyperparameters are defined as "the parameters of the prior". Suppose that one has prior information about a certain parameter $\theta$. ...
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### How to find the posterior distribution and posterior mode of Beta given an exponential prior distribution and binomial data?

This is the question I'm working on: I have already completed part (a). For part (b) i, I modeled y1 given Beta and n1 as Binomial(n1, p1 = d1/(d1+Beta)). For part (b) ii, I showed that the ...
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### Relation Between Bayesian Estimation and Maximum a posteriori estimation

Is maximum a posteriori estimation some kind of Bayesian Estimation? If yes, can you point out other Bayesian estimators? Edit: So I've come to know the following (don't know if they are correct): ...
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### Updating Bayesian prior & likelihood for A/B test

I'm fairly new to bayesian. I'm trying to edit a bayesian python code for $A/B$ test analysis. I'm using uninformative priors as a beta distribution, so my $\alpha$ & $\beta$ parameters are $1$ &...
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### Bayesian approach and ML approach for distributions

Suppose that we have two sets of discrete random variables X ~ f(θ), Y~g(θ) where X and Y are independent, and the parameter θ is the same in both cases. We are interested in predicting Y on the basis ...