Computing the Dirichlet-Multinomial Log-Likelihood Function
arXiv:2007.11967
Abstract
Dirichlet-multinomial (DMN) distribution is commonly used to model over-dispersion in count data. Precise and fast numerical computation of the DMN log-likelihood function is important for performing statistical inference using this distribution, and remains a challenge. To address this, we use mathematical properties of the gamma function to derive a closed form expression for the DMN log-likelihood function. Compared to existing methods, calculation of the closed form has a lower computational complexity, hence is much faster without comprimising computational accuracy.
References in corpus (5)
- Topic Models Conditioned on Arbitrary Features with Dirichlet-multinomial Regression
- A Survey on Practical Applications of Multi-Armed and Contextual Bandits
- A Bandit Approach to Posterior Dialog Orchestration Under a Budget
- Online learning with Corrupted context: Corrupted Contextual Bandits
- Hyper-parameter Tuning for the Contextual Bandit