mcmc Questions
3
Solved
I am trying to learn how to sample truncated distributions. To begin with I decided to try a simple example I found here example
I didn't really understand the division by the CDF, therefore I dec...
Binetta asked 21/12, 2017 at 21:34
7
Solved
Is it possible to import a module with some parameter in python ?
All I mean by parameter is that there exists a variable in the module which is not initialized in that module, still I am using tha...
Airtoair asked 5/6, 2014 at 7:23
1
Solved
Is there a way to construct a matrix with simplex columns in Stan? The model I want to construct is similar to the following, where I model counts as dirichlet-multinomial:
data {
int g;
int c;
...
Zelazny asked 1/10, 2019 at 20:3
2
Solved
I have some code that samples a posterior distribution using MCMC, specifically Metropolis Hastings. I use scipy to generate random samples:
import numpy as np
from scipy import stats
def get_sam...
3
In pymc3 how does one configure a truncated normal prior? In pymc2 it's pretty straightforward (below), but in pymc3 it seems there is no longer a truncated normal distribution available.
Pymc2:
...
2
Solved
Literature says that the metropolis-hasting algorithm in MCMC is one of the most important algorithms developed last century and is revolutional. Literature also says that it is such development in...
Aromaticity asked 28/12, 2018 at 22:16
1
Solved
2
I am looking for a command similar to ranef() used in nlme, lme4, and brms that will allow me to extract the individual random effects in my MCMCglmm model. In my dataset, I have 40 providers and I...
2
Solved
I have used JAGS called via rjags to produce the mcmc.list object foldD_samples, which contains trace monitors for a large number of stochastic nodes (>800 nodes).
I would now like to use R to com...
Sideward asked 15/11, 2015 at 15:54
2
Solved
I need to generate samples from a mixed distribution
40% samples come from Gaussian(mean=2,sd=8)
20% samples come from Cauchy(location=25,scale=2)
40% samples come from Gaussian(mean = 10, sd=6)...
Bushmaster asked 5/5, 2014 at 19:46
1
Solved
I’m wondering if it’s possible to subdivide 3 chains in JAGS on 5 or 6 cores, for example. Here is my code:
library(parallel)
# There is no progression bar using parallel
jags.parallel(data = ...
Aldos asked 24/5, 2016 at 15:50
0
I am fitting some Bayesian linear mixed models using the MCMCglmm package in R. My data includes predictors that are measured with error. I'd therefore like to build a model that takes this into ac...
Carpogonium asked 24/12, 2015 at 2:46
1
Solved
I have a simple hierarchical model with lots of individuals for which I have small samples from a normal distribution. The means of these distributions also follow a normal distribution.
import nu...
1
I have a model that is structured as in this diagram:
I have a population of several people (indexed 1...5 in this picture). Population parameters (A and B, but there can be more) determine the ...
1
Solved
I'm running a Bayesian MCMC probit model, and I'm trying to implement it in parallel. I'm getting confusing results about the performance of my machine when comparing parallel to serial. I don't ha...
Iraqi asked 5/6, 2015 at 14:39
0
I have a list of n observations, each of which is the sum of two Weibull-distributed variables:
x[i] = t1[i] + t2[i]
t1[i] ~ Weibull(shape1, scale1)
t2[i] ~ Weibull(shape2, scale2)
My goal is:
...
Omniscient asked 23/4, 2015 at 20:17
1
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I previously implemented the original Bayesian Probabilistic Matrix Factorization (BPMF) model in pymc3. See my previous question for reference, data source, and problem setup. Per the answer to th...
1
Solved
I've implemented the Bayesian Probabilistic Matrix Factorization algorithm using pymc3 in Python. I also implemented it's precursor, Probabilistic Matrix Factorization (PMF). See my previous questi...
1
Solved
I'm modifying an existing model using RJAGS. I'd like to run chains in parallel, and occasionally check the Gelman-Rubin convergence diagnostic to see if I need to keep running. The problem is, if ...
1
Solved
I'm trying to create a model using the MCMCglmm package in R.
The data are structured as follows, where dyad, focal, other are all random effects, predict1-2 are predictor variables, and response...
1
I'm trying to infer models parameters with PyMC. In particular the observed data is modeled as a sum of two different random variables: a negative binomial and a poisson.
In PyMC, an algebraic com...
0
I'm new to PyMC and trying to fit my non-homogeneous poisson-process with a piecewise-constant rate function using the maximum a posteriori estimate.
My process describes some events during a day....
Franco asked 6/6, 2014 at 14:30
1
Solved
If pymc implements the Metropolis-Hastings algorithm to come up with samples from the posterior density over the parameters of interest, then in order to decide whether to move to the next state in...
1
Solved
I'm updating some calculations where I used pymc2 to pymc3 and I'm having some problems with samplers behavior when I have some discrete random variables on my model. As an example, consider the fo...
8
I am a die hard user of matlab, mostly because this is what I learned first and I have not encountered a problem with a significant enough difference to switch. I come from numerical optimiza...
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