# Questions tagged [prior]

In Bayesian statistics a prior distribution formalizes information or knowledge (often subjective), available before a sample is seen, in the form of a probability distribution. A distribution with large spread is used when little is known about the parameter(s), while a more narrow prior distribution represents a greater degree of information.

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### Bayesian Gaussian mixture - is my prior correct?

I'd like to sample from the Bayesian Posterior of a Gaussian mixture model, but I am not sure about the correct Bayesian formulation of the latter. Is the following correct? I consider the 1-...
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1 vote
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### Reduce Variance of monte carlo estimator using guess of mean

Suppose you have a random variable $X$ and black-box function $f$. Suppose you also have prior estimates $m$ and $s$ of the mean and standard deviation of $f(X)$. How can we use this prior information ...
1 vote
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### Bounded uniform prior in R

I have been fitting a bayesian GLM using brms. The code works well but when I loop this over several data and make it a bit more complex, R encounters a fatal error and crashes. This seems to be ...
72 views

### Parameter distribution of $\theta$ from a rectangular matrix multiplication $C\theta$

I am struggeling to see where this problem fits - i.e. what topics this problem relates to, so I am not able to find the right literature. I want to use some particular information as a prior to a ...
1 vote
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### Importance sampling for a parameterized family of distributions using a wide distribution from the same family

I'm motivated here by a problem for robust Bayesian analysis. Let $l(Y|X)$ be the likelihood and let $\{p_\xi(X)\}$ be a parameterized family of prior distributions where $\xi$ denotes the ...
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### Laplace approximation from a log-posterior in R

I would like to perform a Laplace approximation of a log-posterior. The evolution of a cancer cell at given time $t_j$, $j = 1,\cdots,n$ for an experiment $i$ follows the following Poisson ...
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### Specifying priors over softmax outputs

Looking to train a simple single-layered NN with a N-dim softmax output (and a relatively small feature vector size, ~2-10) in a streaming fashion accumulating K samples in a buffer and then ...
61 views