Protected: Thompson Sampling, linear bandit problem on a logistic regression model
Thompson sampling, linear bandit problem on logistic regression models utilized in digital transformation, artificial intelligence, and machine learning tasks (Thompson sampling, maximum likelihood estimation, Laplace approximation, algorithms, Newton's method, negative log posterior probability, gradient vector, Hesse matrix, Laplace approximation, Bayesian statistics, generalized linear models, Lin-UCB measures, riglet upper bound)