apply_laplacePoisson

Apply the algorithms to make decisions for Thompson sampling Poisson (TS-Poisson) algorithms

A specialized tool is designed for assessing contextual bandit algorithms, particularly those aimed at handling overdispersed and zero-inflated count data. It offers a simulated testing environment that includes various models like Poisson, Overdispersed Poisson, Zero-inflated Poisson, and Zero-inflated Overdispersed Poisson. The package is capable of executing five specific algorithms: Linear Thompson sampling with log transformation on the outcome, Thompson sampling Poisson, Thompson sampling Negative Binomial, Thompson sampling Zero-inflated Poisson, and Thompson sampling Zero-inflated Negative Binomial. Additionally, it can generate regret plots to evaluate the performance of contextual bandit algorithms. This package is based on the algorithms by Liu et al. (2023) <arXiv:2311.14359>.

Tanujit Chakraborty

countts

Thomson Sampling for Zero-Inflated Count Outcomes

Xueqing Liu

Nina Deliu

Lauren Bell

Bibhas Chakraborty

apply_laplacePoisson function

<dl><dt>context</dt>
<dd>context at the current decision time</dd>
<dt>beta_laplacePoisson</dt>
<dd>the randomly sampled Bayesian estimate</dd></dl>

Arguments

Apply the algorithms to make decisions for Thompson sampling Poisson (TS-Poisson) algorithms — apply_laplacePoisson

<dl>

<dt>context</dt>
<dd>context at the current decision time</dd>


<dt>beta_laplacePoisson</dt>
<dd>the randomly sampled Bayesian estimate</dd>

</dl>

apply_laplacePoisson: Apply the algorithms to make decisions for Thompson sampling Poisson (TS-Poisson) algorithms

Description

Usage

Value

Arguments

Examples