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Questions tagged [hierarchical-bayesian]

Hierarchical Bayesian models specify statistical priors on parameters and hyperpriors on the parameters of the prior distributions.

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What is the meaning of bf() in brms package when we do cumulative regression analysis?

I was trying to run a Bayesian multilevel cumulative model on ordinal data and was reading the documentation of brms online. My model looks something like model <- brm(bf(y ~ Condition + (Condition|item) + (Condition|subject)), …
Hanacl_
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Error: non-constant-expression cannot be narrowed from type 'npy_intp' to 'int'

I am trying to run the following model, but it fails during compilation: import numpy as np import pymc3 as pm def sample_data(G=1, K=2): # mean proportion ([0,1]) for each g p_g = np.random.beta(2, 2, size=G) # concentration around…
merv
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How to indicate an observation is the larger of two sampled values?

I'm writing a JAGS script (hierarchical bayesian model) where the times of events are modelled as a race between two processes. Observations: time is the measured times of events. Model: two processes with gaussian rates - whichever process…
Sanjay Manohar
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BayesianOptimization fails due to float error

I want to optimize my HPO of my lightgbm model. I used a Bayesian Optimization process to do so. Sadly my algorithm fails to converge. MRE import warnings import pandas as pd import time import numpy as np warnings.filterwarnings("ignore") import…
mugdi
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Bayesian Calibration with PyMC3, Kennedy O'Hagan

I'm quite new to probabilistic programming and pymc3... Currently, I want to implement the Kennedy-O’Hagan framework in pymc3. The setup is according to the paper of Kennedy and O'Hagan as follows: We have n observations zi of the form zi = f(xi ,…
DavidGoebel
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brms hurdle_lognormal model: is this a correct way to convert posterior values into y-variable units and hurdle values into probabilities?

I have a zero-inflated y-variable. Could you please confirm that my solutions for converting brms hurdle lognormal posterior values into meaningful estimates as y-variable units (lognormal part) or zero probabilities (hurdle part). Model model =…
st4co4
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Error in MCMCglmm

I'm having some troubling running a mixed model in MCMCglmm. It works fine when I try to run it as a linear regression: Model.Fit = MCMCglmm(Y~X1, random = ~Cluster, data = DataSet) But I can't seem to get it to run a logistic or ordinal…
TPM
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Interpretation of variables in multi-level regression with random effects

I have a dataset that looks like the one below (first 5 rows shown). CPA is an observed result from an experiment (treatment) on different advertising flights. Flights are hierarchically grouped in campaigns. campaign_uid flight_uid treatment …
Amelio Vazquez-Reina
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Writing the Jeffreys prior for a vector variable in Stan?

I am trying to model the Jeffreys prior for error variance in a heteroscedastic ANOVA design in rstan. That is to say, . Is the following setting correct? target += -2 * sum(log(sigma)); where the vector variable is declared as vector[C]…
Dexter SherloConan
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How to insert for loop inside pm.Deterministic() to create an array in pymc3 model?

with pm.Model() as model: w = pm.Uniform('w', 0, 1) c = pm.Uniform('c', 0, 5) b = 0.5 s = pm.Deterministic('s', pm.math.exp(-c * (w * d1 + (1-w) * d2))) s_A = np.zeros((8)) s_A = pm.Deterministic('s_A', for i in range(8) : s_A[i] =…
Harshit Jain
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Tensorflow probability - MCMC - problems with bijectors in transition kernel?

I'm building up a mixture of models in tensorflow-probability. The joint distribution for one given model is: one_network_prior = tfd.JointDistributionNamed( model=dict( mu_g=tfb.Sigmoid( low=-1.0, high=1.0, …
Louis
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How to code priors for a hurdle-lognormal() brms model?

I understand the prior concept in Bayesian, which is cool, but their turning into code is too hard. As much as I know, publishing without the priors isn't a good practice. I have quite large dataset, n = 5000, thus very mildly informative priors…
st4co4
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Hierarchical Linear Regression with N-dimension sample

In this example from PyMC3 (https://docs.pymc.io/notebooks/GLM-hierarchical.html) there is estimation of posterior distribution with 1 dimensional sample (samples from dataset have only one feature). # Hyperpriors for group nodes mu_a =…
Lorenzo Vannucchi
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Hierarchical model in Tensorflow probability using JointDistributionSequential

I'm trying to understand how to implement the following model in Tensorflow probability. An angle, theta, has uniform prior probability in the range [-pi / 2, +pi / 2]; A direction-flip probability, beta, has uniform prior probability in range [0,…
tobycoleman
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PYMC3 Mixture model: help understanding multiple variables model

Let's say I have a dataframe with 4 variable. I want to see if I can generate a posterior of gamma mixtures over all the variables, with the goal to find clusters for each observation. I'm guessing I will need some sort of multivariate gamma…
James Stirling
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