2008/02/14 by Wouter M. Koolen, Wouter Koolen, Koolen, Wouter +2
Computer Science · Mathematics · #Bayesian Modeling and Causal Inference #Data Structures and Algorithms (cs.DS) #FOS: Computer and information sciences #G.3 #Information Theory (cs.IT) #Machine Learning (cs.LG) #Machine Learning and Algorithms #Topic Modeling #cs.DS #cs.IT #cs.LG #math.IT
paper · pdf · doi:10.48550/arxiv.0802.2015
50 pages
openalex publication_date 2008/02/14 · arxiv created 2008/02/15 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/04
We show how models for prediction with expert advice can be defined concisely and clearly using hidden Markov models (HMMs); standard HMM algorithms can then be used to efficiently calculate, among other things, how the expert predictions should be weighted according to the model. We cast many existing models as HMMs and recover the best known running times in each case. We also describe two new models: the switch distribution, which was recently developed to improve Bayesian/Minimum Description Length model selection, and a new generalisation of the fixed share algorithm based on run-length coding. We give loss bounds for all models and shed new light on their relationships.