We study model-based reinforcement learning (RL) for episodic Markov decision processes (MDP) whose transition probability is parametrized by an unknown core with features of state and action. Despite much recent progress in analyzing algorithms the linear MDP setting, understanding more general models very restrictive. In this paper, we propose a provably efficient RL algorithm given multinomi...

The paper proposes a Bayesian multinomial logit model to analyse spatial patterns of urban expansion. specification assumes that the log-odds each class follow autoregressive proce...

Using a multi-layer multinomial Bayesian network, we study the interplay between Bayesian inference and natural image learning in relation to receptive field properties of early visual cortex. Keywords—Bayesian inference, natural image learning

A strategy revision process in symmetric normal form games is proposed. Following Kandori, Mailath, and Rob (1993), members of a population periodically revise their strategy choice, and choose a myopic best response to currently observed play. Their payoffs are perturbed by normally distributed Harsanyian (1973) trembles, so that strategies are chosen according to multinomial probit probabilit...

The multinomial logit model (MNL) possesses a latent variable representation in terms of random variables following a multivariate logistic distribution. Based on multivariate finite mixture approximations of the multivariate logistic distribution, various data-augmented Metropolis-Hastings algorithms are developed for a Bayesian inference of the MNL model. Zusammenfassung: Das multinomiale log...