# bayesian's questions - Chinese 1answer

4.502 bayesian questions.

### 1 calculating or approximating the normalizing constant bayesian posterior

1 answers, 17 views bayesian posterior

### 3 Advice for conducting the same experiment several times?

I'm very new to Bayesian statistics, but I've found myself in a situation where they might be able to help. I'll be running the same experiment repeatedly and analyzing the data every few months. The ...

### 3 How to extend Solomonoff's universal prior to stochastic models?

1 answers, 71 views bayesian algorithms prior
Solomonoff's universal prior for models is based on the algorithmic complexity of a computer program $p$ which executes that model. Where $l$ is the length of the computer program, the prior is ...

### 2 Discrete Kernel for Sequential Monte Carlo (population monte carlo)

I'm attempting understand, and use, the population Monte Carlo algorithm found here https://arxiv.org/abs/0805.2256 for approximate Bayesian computation. However I think this is a general SMC question,...

### beta binomial hierarchical model with two groups, inference on the group hyperparameters

The problem I want to solve: Lets imagine that I have two factories A and B, where each factory produces coins. What I suspect is that the probability of tails (denoted as $\theta$) varies ...

### 1 Help with computing message on TrueSkill factor graph

I want to better understand the step for calculating the message from the game factor $h_{g}$ down to the difference variable $d_g$ on the TrueSkill factor. Such message is shown in the Rasmussen's ...

### 7 Maximum likelihood is not re-parametrization invariant. So how can one justify using it?

3 answers, 799 views bayesian maximum-likelihood frequentist
There is something that is confusing me about max-likelihood estimators. Suppose my I have some data and the likelihood under a parameter $\mu$ is $$L(D|\mu) = e^{-(.7-\mu)^2}$$ which is ...

### 2 Defining prior on variance and not precision

1 answers, 483 views bayesian winbugs
I know that WinBugs uses precision as a parameter in dnorm instead of variance ...

### Model Selection: Goodness-of-Fit Statistic when Noise is Unknown (vs Reduced Chi-Squared)

I have data D_k and different models M_i, and I would like to calculate a goodness-of-fit statistic for undertaking model comparison between the different M_i's, in the case of unknown uncertainties ...

### P-values Calculation as significance (Pseudo-counts and Hypergeometric)

0 answers, 12 views bayesian p-value conjugate-prior
I am looking for a way to solve this problem I have run k-means to obtain a set of clusters with elements, some of this clusters have 1 or 2 elements in them. I use the hypergeometric function to ...

### 8 What is a “strictly positive distribution”?

4 answers, 2.190 views self-study bayesian
I am reading Judea Pearl's "Causality" (second edition 2009) and in section 1.1.5 Conditional Independence and Graphoids, he states: The following is a (partial) list of properties satisfied by the ...

### Informative priors

0 answers, 24 views bayesian prior hierarchical-bayesian
I have a general query regarding informativeness of priors, since my laptops gone down and not able to run this on Stan to check (but from previous runs I think this was the case). If the priors used ...

### Likelihood term in Bayesian inferencing versus the general definition

2 answers, 33 views bayesian inference likelihood parametric

### 4 Can anyone tell me why we always use the Gaussian distribution in Machine learning?

For example, we always assumed that the data or signal error is a Gaussian distribution? why? I have asked this question on stackoverflow, the link: https://stackoverflow.com/questions/12616406/...

### 4 Combining Posterior Distributions of Separate Models

1 answers, 350 views bayesian posterior jags stan
I am running Bayesian models to estimate the number of fruits on a plant, given the presence/absence of herbivores. I get a posterior distribution on each mean. I then run a separate model to estimate ...