arXiv:1810.07435
EMHMM Simulation Study
원문 초록
Eye Movement analysis with Hidden Markov Models (EMHMM) is a method for modeling eye fixation sequences using hidden Markov models (HMMs). In this report, we run a simulation study to investigate the estimation error for learning HMMs with variational Bayesian inference, with respect to the number of sequences and the sequence lengths. We also relate the estimation error measured by KL divergence and L1-norm to a corresponding distortion in the ground-truth HMM parameters.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Statistical Machine Learning for Astronomy -- A Textbook
A Simple Approximate Bayesian Inference Neural Surrogate for Stochastic Petri Net Models
Scalable Bayesian Low-Rank Adaptation of Large Language Models via Stochastic Variational Subspace Inference
Generative Diffusion Receivers: Achieving Pilot-Efficient MIMO-OFDM Communications
Variational Inference with Mixtures of Isotropic Gaussians
ALINE: Joint Amortization for Bayesian Inference and Active Data Acquisition
토론
이 논문과 연결된 포럼 글이 아직 없습니다.