I used glmmTMB to run GLMMs with a beta distribution to evaluate the proportion of time an animal spends doing a behavior. My observations were of different lengths, and I weighted the observations ...

I've got a problem at work that goes as follows: We have, say, 1 million possible events which define frequency-severity distributions. For each event we have an annual rate which defines a Poisson ...

I'm working with proportion data and have tried several models to address issues of overdispersion and underdispersion. Initially, I used a binomial distribution model which showed overdispersion. ...

I am trying to integrate chromatographic peaks. I need to be able to model the peak shape to consistently determine when the peaks begin and end. I can model Gaussian peaks but many peaks are ...

200 people were tested, 20 of those were infected. I want to get a posterior distribution for the uncertainty associated with the probability that a person is infected. I do this like this: n<-200 ...

I am trying to estimate parameters in a beta regression using optim in R. I can do so if I parameterize the model with mu and phi as is done in the package betareg. That optim code is shown below. ...

I would like to estimate the alpha and beta parameters from a beta distribution when all I have is a set of probabilities. I cannot seem to find an example of this online. Below I try three ...

For those without a statistics background but who could assist with coding: I have multiple small datasets (around 30 to 200 rows each) and need to determine the best-fitting distribution. One ...

It might be a simple question but I have been trying to fix this issue for a few days so any help is really appreciated. If there is something wrong about my question, please leave a comment so I know ...

Lets say I have the following data library(ggplot2) set.seed(2203) data <- runif(100, 15, 45) df <- data.frame(data) And my end goal is to overlay 3 different distributions on the histogram ...

I have the written the following code in R with an aim to generate correlated random variables which follow the beta distribution #objective is to generated correlated #beta distributed data ...

I am working with data of vegetation cover (proportions) for different height strata (0-5, 5-15, 15-30, >30 cm, and also bare ground) amongst four different sites (sitio) and two different time ...

I'm trying to use TensorFlow Probability to learn the alpha and beta parameters of a beta distribution. I can't get it to work for some reason - the loss is all NaN values. Here's what I have: from ...

I'd like to specify a truncated beta distribution such that the support is [0.1, 0.4], and allow for the probability density at the lower bound to be higher than very near 0. Here's what I have so far....

I'd like to construct a beta distribution where the mu and sigma are 0.28 and 0.003, respectively, and the distribution is bound at [0.1, 0.5]. import numpy as np import pandas as pd import plotly....