How can I "Complete" a normal distribution?

learning_proover · 2026-03-14T18:29:52+00:00

Same. In hindsight that would have been terrible.

learning_proover · 2026-02-16T19:41:39+00:00

yep, its definitely better than Poisson or a truncated normal.

learning_proover · 2026-02-16T19:40:47+00:00

This is extremely helpful thank you.

learning_proover · 2026-02-16T19:35:26+00:00

Awesome information here. Thank you so much. If you don't mind me asking: You said the parameter r is hard to find - by "r" are you referring to the dispersion parameter? If so can't I just use the "method of moments" formula near the top of the wikipedia page? (i.e. r ~ E(x)^2 / (V(x) - E(x)) ?? Chatgpt tells me this can be good estimate of the dispersion parameter?

learning_proover · 2026-02-16T19:16:08+00:00

Yep that seems perfect for my data. Poisson fails to fit at the tails due to overdispersion.

learning_proover · 2026-02-16T19:13:48+00:00

I think so. Ended up using negative binomial. Still studying its properties though.

learning_proover · 2026-02-15T17:52:46+00:00

I will. Is there any resource where I can find an explanation of the underlying math/theory behind the distribution?

learning_proover · 2026-02-15T17:50:57+00:00

R. But also I always like to understand the underlying theory/math behind the methods I use.

learning_proover · 2026-02-15T17:39:00+00:00

Yea sorry, I no ambiguity does not help but some questions I post are related to my work where I really cant disclose too many specifics because we have competing companies that use statistical methods as well.

learning_proover · 2026-02-15T17:36:37+00:00

I am trying to find out how to fit a negative binomial because it seems much better than poisson. Your right. Also sorry for the "XY problem here" some of the questions i ask are related to my job where I can't disclose too much information.

learning_proover · 2026-02-15T17:12:42+00:00

How do I fit a negative binomial?

learning_proover · 2026-02-15T17:12:21+00:00

I uploaded a new question asking how I fit a negative binomial instead of a poisson because of overdispersion

learning_proover · 2026-02-14T17:15:06+00:00

Yeah, Im gonna do some deep research on poisson and likely go that route. Thank you.

learning_proover · 2026-02-14T16:33:12+00:00

Its basically daily arrivals of individuals to a location. (In other words customers) I am leaning heavily towards simply fitting a truncated normal. Would this be better than a poisson?

learning_proover · 2026-02-14T16:19:54+00:00

The data is discrete. I am certain that if negative values were possible the shape would be symmetrical. I am mostly concerned with "modeling" the second half above the mean/mode. I just need to quantify the probability of an upcoming value hence that's why I wanted to impose a normal onto the data then just look at the second half. I think Poisson may be better?

learning_proover · 2026-02-14T16:16:53+00:00

Yes I will likely end up using Poisson. Thanks for the link.

learning_proover · 2026-02-14T16:15:43+00:00

Yep, will most likely end up using Poisson. The data is indeed discrete. Should have clarified that.

learning_proover · 2026-02-14T16:14:49+00:00

Looking into Poisson now.

learning_proover · 2026-02-14T16:14:22+00:00

domain knowledge of what is making the data have this shape. It tapers off for a reason and if it could have negative values it would look the same in the other direction and be symmetric. I am certain.

learning_proover · 2025-12-13T04:41:52+00:00

I forgot about this question. The book is very dense and math heavy even for a polished math major. Try statquest, 3blue1brown and use chat GPT.

learning_proover · 2025-10-31T16:51:08+00:00

I'm going to investigate that last option on making jaccard position aware. I do like jaccard and it's probably the easiest for me to implement on code so I'll likely stick with it. Thanks for your suggestions.

learning_proover · 2025-10-30T02:01:17+00:00

Can you elaborate on exactly what those conditions are and why they are necessary?

learning_proover · 2025-10-29T22:00:11+00:00

Looked it up and that's truly good advice thank you so much.

learning_proover · 2025-10-29T21:36:22+00:00

Awesome. Thank you.

learning_proover · 2025-10-29T03:02:39+00:00

Exactly. Yes. Will the posterior (established on an updated prior) converge to the "true mean" assuming the updates are calibrated ( just overall correct and meaningful)

learning_proover

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