@inproceedings{
    Luttinen13ecml,
    author = "Luttinen, Jaakko",
    isbn = "978-3-642-40987-5",
    series = "Lecture Notes in Computer Science",
    abstract = "This paper presents a fast variational Bayesian method for linear state-space models. The standard variational Bayesian expectation-maximization (VB-EM) algorithm is improved by a parameter expansion which optimizes the rotation of the latent space. With this approach, the inference is orders of magnitude faster than the standard method. The speed of the proposed method is demonstrated on an artificial dataset and a large real-world dataset, which shows that the standard VB-EM algorithm is not suitable for large datasets because it converges extremely slowly. In addition, the paper estimates the temporal state variables using a smoothing algorithm based on the block LDL decomposition. This smoothing algorithm reduces the number of required matrix inversions and avoids a model augmentation compared to previous approaches.",
    il = "no",
    eventdetails = "EVENT DETAILS",
    year = "2013",
    title = "Fast Variational {Bayesian} Linear State-Space Model",
    booktitle = "Machine Learning and Knowledge Discovery in Databases, ECML/PKDD'2013",
    juforank = "NA",
    unitcode = "T306-100",
    impactfactor = "A4",
    responsibleauthor = "Luttinen, Jaakko",
    volume = "8188",
    address = "Berlin Heidelberg",
    kay = "NA",
    pages = "305--320",
    publisher = "Springer",
    doi = "10.1007/978-3-642-40988-2_20",
    language = "eng",
    url = "http://dx.doi.org/10.1007/978-3-642-40988-2_20",
    corerank = "",
    flags = "public COIN",
    pdf = "luttinen_ecml13.pdf"
}