# bayesian's questions - Chinese 1answer

4.625 bayesian questions.

### Comparing 2 Bayesian Models with different structure

I'm a bit new to Bayesian statistics so please bear with me if this question is trivial. Let's say I have $100$ observations for $2$ Bernoulli variables $X$ and $Y$. I notice that they have the ...

### 1 ABC: Population Monte Carlo (PMC) convergence statistics?

0 answers, 8 views bayesian convergence abc
I'm using the abcpmc code: Approximate Bayesian Computing (ABC) Population Monte Carlo (PMC) implementation based on Sequential Monte Carlo (SMC) with Particle Filtering techniques. described in ...

### Bayes Estimator

1 answers, 51 views bayesian estimators
Wikipedia has a section on the Bayes Estimator. https://en.wikipedia.org/wiki/Bayes_estimator Isn't Bayes Estimator simply the value of the parameter that minimizes the expected loss of a loss ...

### what does p( y | μ,σ²) really mean?

Just started to study Bayesian Statistics. I am very confused the concept of having a conditional probability on a distribution. Specifically: I understand what p( A | B ) where A="I am sick" and ...

### Survival time problem exponential with gamma prior

The survival times, in days, of patients diagnosed with a severe form of a terminal illness are thought to be well modelled by an exponential($\theta$) distribution. We observe the survival times ...

### 1 Do the parameters that arise in de Finettis representation theorem follow the rules of probability?

I recently stumbled upon de Finettis (pretty cool) representation theorem (What is so cool about de Finetti's representation theorem?). I wondered whether the RV $\Theta$ that arises in this ...

### 2 When does knowing the causal structure of the data generating process improve supervised learning?

0 answers, 18 views machine-learning bayesian causality
Consider a supervised learning prediction task where we have some real-valued feature vector X and wish to train a model that predicts discrete class label Y. When the model is deployed, Y will be ...

### Bayesian hyperparameter learning in a multi-ouput Gaussian Process Regression

1 answers, 18 views regression bayesian gaussian-process
Let's imagine I have the following equation $y_t=f(x_t)+e_t$, where $f(x)$ follows a gaussian process, and $e_t\sim N(0,\Sigma)$. How does one go about to learn the hyperparameters, i.e., $\Sigma$ ...

### 3 Understanding bayesian model code from chapter 4 of “Statistical rethinking”

1 answers, 48 views self-study bayesian

### 3 Clarification on LDA and the multivariate Gaussian

From my understanding, to calculate the posterior probability of a sample $x$ belonging to a class $k$ using Linear Discriminant Analysis you would first calculate the eigenvector matrix $W$ required ...

### 1 How to choose an appropriate variational distribution?

I work in deep learning research and I am trying to learn how to use variational inference in order to approximate a posterior over the learned weights. I have looked extensively at Yarin Gal's ...

### How Probable is a Set? [closed]

0 answers, 111 views bayesian categorical-data python binomial
I'm struggling to articulate my question: Introduction Work Orders (WOs) are instructions to a technician to perform specific maintenance actions on a specific piece of machinery. Each work order ...

### 5 Algorithm for approximating a density by a mixture density

Given a density $f(x)$ (e.g. the log-normal distribution or log-$t_{\nu=3}$ distribution), I was wondering what algorithm are known/typically used to find a mixture of distributions $g_r(x)$ from ...

### 1 How to understand the following Bayesian schema?

0 answers, 13 views bayesian conditional-probability
My knowledge of probability is basic, and I understand the point of Bayesian interpretation most roughly. The following is part of this paper. It is about how p can be rational for person 1 and not-p ...

### 2 Conjugate priors for dynamic model $x_{t+1}=Ax_{t}+\eta_t$

1 answers, 63 views bayesian multivariate-regression

### 2 How worried should I be about low acceptance rate in cold chain (parallel tempering MCMC sampler)

I have a very noisy/multimodal likelihood function for a 6-parameter model. The popular emcee sampler fails miserably (no matter how many chains I use and for how ...

### 8 Can a proper prior and exponentiated likelihood lead to an improper posterior?

2 answers, 310 views bayesian prior posterior
(This question is inspired by this comment from Xi'an.) It is well known that if the prior distribution $\pi(\theta)$ is proper and the likelihood $L(\theta | x)$ is well-defined, then the posterior ...

### Use of svyglm for a weighted regression in a bayesian framework

1 answers, 27 views regression bayesian propensity-scores
I am using the twang package in R to balance two groups by creating propensity scores, which are then used as weights in the svyglm for a weighted regression of the two groups. I would like however ...

### 2 Bayesian p-value in wrong direction using step function in JAGS / BUGS

1 answers, 23 views bayesian p-value posterior jags bugs
I have estimated a Weibull regression model in JAGS using rjags and R2JAGS. The estimated posterior predictive p-values using the step() function confuse me. They make sense (comparing them to lower ...

### How to use Pymc3 to create a Uniform and Von Mises mixture model [closed]

0 answers, 5 views bayesian mixture
I am new to Pymc3 and currently trying to do an parameter estimation with it. I have a set of data which is assumed as a mixture of unform distribution and Von-mises. I found that the available ...

### What is the relation between the effective sample size $n$ and the model dimension (the effective size of parameters) $p$ in Bayesian model selection?

What is the relation between the effective sample size $n$ and the model dimension (the effective size of parameters) $p$ in Bayesian model selection? Or is there any articles talking about this? I ...

### 1 Sampling Bayes factors under the null hypothesis to estimate a threshold of “significance” for hypothesis testing

Context: I have a psychology experiment with a 2 x 2 design (with Condition (label, no label) and ContrastType (head, tail) as ...

### 1 What is meant by “non-convex prior” and “sparsity-inducing prior”?

1 answers, 327 views bayesian prior dirichlet-distribution
I was reading how to use collapsed gibbs sampling for latent dirichlet allocation in a google group and one user talked about using dirichlet priors with small hyperparameters and sum out the z ...

### Calculating Cauchy prior

0 answers, 19 views r bayesian t-test cauchy
I've recently used the package BayesFactor in R with the default priors scale r. I have been advised to adjust the Cauchy width based on some pilot data rather than ...

### 1 Objective function of Bayesian Model Averaging

I am quite confused about the objective function of the bayesian model averaging in the paper "Bayesian Averaging of Classifiers and the overfitting Problem".1 On the section 2, here is the first ...

### 2 Proper prior leading to improper posterior

1 answers, 87 views bayesian posterior
Preface I must say I am aware of previous discussions (e.g. this one) and also of this excellent, didactic proof using Fubini's theorem as presented by Jared Niemi [I'm not saying Jared Niemi is the ...

### What can I conclude about the distribution of wrong phone numbers?

Let's say I have a list of 100 phone numbers. I call them all. Nobody picks up for 70. I get someone on the line for 30. Of those, 10 are wrong numbers. What can I conclude about the distribution of ...

### 1 If Bayesian approaches are better than frequentist then how can it be as practical?

In a textbook Probability Theory: The Logic of Science written by E. T. Jaynes and others, on page 13 it reads that: For many years, there has been controversy over ‘frequentist’ versus ‘...

### SMOTE in Bayesian Networks

Oversampling or SMOTE is useful when the data is imbalanced. Here is the question I cant find the answer: Since we are dealing with probabilities in Bayesian Networks (probabilistic graphical model), ...