For all non-multinomial distributions, we should use pomegranate to fit mixture models of the appropriate components pending the data type (e.g., positive only, discrete, normal, etc.) to the data then serialize the mixture model parameters rather than anything derived from raw data, and sample from the mixture models during generation. This will help with #20 and allow us to capture richer dataset dynamics and better visualize dataset properties with more concise, meaningful configuration files.
See https://chatgpt.com/share/67c6267e-a498-800c-8f0b-00463bc44656 for a discussion with ChatGPT on different technical approaches.
For all non-multinomial distributions, we should use pomegranate to fit mixture models of the appropriate components pending the data type (e.g., positive only, discrete, normal, etc.) to the data then serialize the mixture model parameters rather than anything derived from raw data, and sample from the mixture models during generation. This will help with #20 and allow us to capture richer dataset dynamics and better visualize dataset properties with more concise, meaningful configuration files.
See https://chatgpt.com/share/67c6267e-a498-800c-8f0b-00463bc44656 for a discussion with ChatGPT on different technical approaches.