loglikelihood | A library for python to implement the 'Log Likelihood

I want to estimate parameters of negative binomial distribution using MCMC Metropolis-Hastings algorithm. In other words, I have sample:

I am trying to create a structural equation model that tests the structure of latent variables underlying a big 5 dataset found on kaggle. More specifically, I would like to replicate a finding which suggests that common method variance (e.g., response biases) inflate the often observed high intercorrelations between the manifest variables/items of the big 5 (Chang, Connelly & Geeza (2012).

I have a data frame where certain columns contain the error and warning messages from Mplus. The text is saved in a weird format, so rather than trying to process each message, I was hoping to simply count the number of messages by counting the occurrences of c(\ in the cell as it is the unique character combination that appears before each warning or error.

For example, one cell contains the messages:

I'm trying to include a black box likelihood function in a pymc3 model. This likelihood function just takes a vector of parameter values and returns the likelihood (all data is already included in the function).

So far I've been following this guide and have modified the code as follows to accommodate the fact my model only has one parameter k.

I have a function that takes in lambda and a data sample, and finds the corresponding log-likelihood value for each data point:

I'm performing Data Science and am calculating the Log Likelihood of a Poisson Distribution of arrival times.

I am trying to run the code below in VS Code for Julia (or directly on Julia). It is a simple example that computes the Maximum Likelihood estimator of the mean and the variance of a normal distribution (source):

I am trying to calibrate a model using pykalman and the scipy optimiser. For some reasons scipy seem to think that my input is a masked array, but it is not. I have added the code below:

I was trying to evaluate a customized function over every point on an n-dimensional grid, after which I can marginalize and do corner plots.

This is conceptually simple but I'm struggling to find a way to do it efficiently. I tried a loop regardless, and it is indeed too slow, especially considering that I will be needing this for more parameters (a1, a2, a3...) as well. I was wondering if there is a faster way or any reliable package that could help?

EDITS: Sorry that my description of myfunction hasn't been very clear, since the function takes some specific external data. Nevertheless here's a sample function that demonstrates it: