When to Use Poisson vs. Negative Binomial Regression: A Practical Guide

Poisson regression models count data assuming equal mean and variance, while negative binomial regression handles over‑dispersion by adding a gamma‑distributed heterogeneity term; this article explains their assumptions, likelihoods, interpretation of incidence rate ratios, and guidance on choosing the appropriate model with robust standard errors.

MLEPoisson regressioncount data

0 likes · 6 min read

When to Use Poisson vs. Negative Binomial Regression: A Practical Guide

Code DAO

Dec 16, 2021 · Fundamentals

How Poisson Hidden Markov Models Enable Count‑Based Time‑Series Regression

This article explains how mixing a Poisson process with a discrete k‑state hidden Markov model creates a Poisson HMM that captures autocorrelation in integer‑valued time‑series, detailing the model formulation, prediction via expectation over states, and parameter estimation using MLE or EM.

EMMLEMarkov model

0 likes · 11 min read

How Poisson Hidden Markov Models Enable Count‑Based Time‑Series Regression

Python Programming Learning Circle

Apr 20, 2020 · Fundamentals

Understanding Binomial Distribution, Permutations, Combinations, and Their Python Implementations

This article introduces the fundamentals of binomial and Bernoulli distributions, explains permutations and combinations, provides Python functions to compute them, demonstrates probability calculations and visualizations with matplotlib and plotly, and shows a maximum likelihood estimation example for binomial parameters.

MLEbinomial distributioncombinatorics

0 likes · 8 min read

Understanding Binomial Distribution, Permutations, Combinations, and Their Python Implementations

MaGe Linux Operations

Aug 6, 2018 · Fundamentals

Estimating Coin Toss Probability with Maximum Likelihood in Python

This article explains the concept of maximum likelihood estimation (MLE) and demonstrates how to estimate the probability of heads in a coin‑toss experiment using Python simulation, sympy for the likelihood function, and visualisation of the resulting discrete probability distribution.

Coin TossMLEMaximum Likelihood

0 likes · 5 min read

Estimating Coin Toss Probability with Maximum Likelihood in Python