SS-L6.3
A GIBBS SAMPLER FOR BAYESIAN NONPARAMETRIC STATE-SPACE MODELS
Christos Merkatas, Simo Särkkä, Aalto University, Finland
Session:
SS-L6: Graphical Inference and Modeling in Dynamical Systems Lecture
Track:
Special Sessions
Location:
Room 205B
Presentation Time:
Wed, 17 Apr, 17:10 - 17:30 (UTC +9)
Session Co-Chairs:
Victor Elvira, University of Edinburgh and Emilie Chouzenoux, Inria Saclay
Session SS-L6
SS-L6.1: Graphical Inference in Non-Markovian Linear-Gaussian State-space Models
Emilie Chouzenoux, Inria Saclay, France; Victor Elvira, University of Edinburgh, United Kingdom of Great Britain and Northern Ireland
SS-L6.2: KALMAN FILTER FOR TRACKING NETWORK DYNAMIC
Lital Dabush, Tirza Routtenberg, Ben-Gurion University of the Negev, Israel
SS-L6.3: A GIBBS SAMPLER FOR BAYESIAN NONPARAMETRIC STATE-SPACE MODELS
Christos Merkatas, Simo Särkkä, Aalto University, Finland
SS-L6.4: INFERENCE OF TIME–VARYING GRAPH TOPOLOGIES VIA GAUSSIAN PROCESSES
Chen Cui, Petar Djuric, Stony Brook University, United States of America
SS-L6.5: Joint Signal Recovery and Graph Learning from Incomplete Time-Series
Amirhossein Javaheri, Hong Kong University of Science and Technology/Sharif University of Technology, Hong Kong; Arash Amini, Farokh Marvasti, Sharif University of Technology, Iran (Islamic Republic of); Daniel P. Palomar, Hong Kong University of Science and Technology, Hong Kong
Contacts