# Tagged Questions

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### Posterior covariance of Normal-Inverse-Wishart not converging properly

I am trying to implement a simple normal-inverse-Wishart conjugate prior distribution for a multivariate normal with unknown mean and covariance in numpy/scipy such that it can take a data vector and ...
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### Confidence intervals and central estimates for a functional of an estimated function with uncertain parameters

I've got a problem that is leading me to dip my toes into Bayesian stats, and I've got a question about confidence (or, I suppose, credible) intervals: Say you want to know how $X$ maps to $y$. You ...
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### Finding the full conditonal distribution when there are multiple distributions involved

6 neighboring countries have the following disease instances: $y = (y_1, y_2,...,y_n)$ with a population of $x = (x_1, x_2,...,x_n)$. The following model and prior distributions are considered: ...
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### Simulating from Posterior Predictive Over Many Periods

Suppose I obtain a posterior distribution through MCMC. This will give some simulated values for the parameters to a statistical model. If I calculate the posterior predictive distribution one period ...
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### How does the beta prior affect the posterior under a binomial likelihood

I have two questions, Question 1: How can I show that the posterior distribution is a beta distribution if the likelihood is binomial and the prior is a beta Question 2: How does choices the prior ...
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### Which distributions are parameterization invariant when based on the Jeffreys prior?

I understand that the Jeffreys prior provides a method for constructing a prior distribution over parameters for a given model (likelihood function) such that the prior distribution is "invariant ...
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### exponential prior for a gamma distribution

I am planing to make a bayesian inference for a scale mixture of normal distributions. We know the fact the the t-distribution with df $\nu$ can be expressed as a scale mixture of normal ...
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### How to choose the tolerance parameter for ABC?

I have the following sorted data (sampling from parametric space [1,5]) with respect to their distances of parameter Theta. i.e., Let say N = 1000, Theta : 1.1, 1.7, 1.9, 2.4, 2.8, . . . , 4.9 ...