Predicting atmospheric particle formation days by Bayesian classification of the time series features
Authors
Institute for Atmospheric and Earth System Research/Physics, Helsinki University; Department of Applied Physics, Aalto University; Aalto Science Institute, School of Science, Aalto University
Department of Applied Mathematics and Computer Science (DTU Compute), Technical University of Denmark, Kongens Lyngby
Institute for Atmospheric and Earth System Research/Physics, Helsinki University, FI; Institute of Physics, University of Tartu, Tartu
Institute for Atmospheric and Earth System Research/Physics, Helsinki University
Institute for Atmospheric and Earth System Research/Physics, Helsinki University, FI; Aerosol and Haze Laboratory, Beijing University of Chemical Technology, Beijing
Department of Applied Physics, Aalto University; Laboratory of Physics, Tampere University of Technology
Department of Applied Physics, Aalto University, FI; WPI Nano Life Science Institute (WPI-NanoLSI), Kanazawa University, Kakuma-machi, JP; Graduate School Materials Science in Mainz
DOI: https://doi.org/10.1080/16000889.2018.1530031 | Journal eISSN: 1600-0889
Language: English
Page range: 1530031 - 1530031
Submitted on: Aug 30, 2017
Accepted on: Sep 11, 2018
Published on: Jan 1, 2018
Published by: Stockholm University Press
In partnership with: Paradigm Publishing Services
Keywords:
© 2018 M. A. Zaidan, V. Haapasilta, R. Relan, H. Junninen, P. P. Aalto, M. Kulmala, L. Laurson, A. S. Foster, published by Stockholm University Press
This work is licensed under the Creative Commons Attribution 4.0 License.