Corpus ID: 219401594

Exact inference for a class of non-linear hidden Markov models

  title={Exact inference for a class of non-linear hidden Markov models},
  author={G. K. K. King and O. Papaspiliopoulos and M. Ruggiero},
  journal={arXiv: Computation},
  • G. K. K. King, O. Papaspiliopoulos, M. Ruggiero
  • Published 2020
  • Computer Science, Mathematics
  • arXiv: Computation
  • Exact inference for hidden Markov models requires the evaluation of all distributions of interest - filtering, prediction, smoothing and likelihood - with a finite computational effort. This article provides sufficient conditions for exact inference for a class of hidden Markov models on general state spaces given a set of discretely collected indirect observations linked non linearly to the signal, and a set of practical algorithms for inference. The conditions we obtain are concerned with the… CONTINUE READING

    Figures from this paper


    Nonparametric inference in hidden Markov models using P-splines.
    • 40
    • PDF
    Bayesian non-parametric hidden Markov models with applications in genomics
    • 98
    • PDF
    Optimal filtering and the dual process
    • 11
    • PDF
    Statistical Inference in Hidden Markov Models Using k-Segment Constraints
    • 13
    • PDF
    Bayesian Higher Order Hidden Markov Models
    • 1
    • PDF
    Bayesian Filtering and Smoothing
    • Simo Särkkä
    • Computer Science
    • Institute of Mathematical Statistics textbooks
    • 2013
    • 877
    • PDF
    Calculating posterior distributions and modal estimates in Markov mixture models
    • 352
    • PDF
    Computable infinite-dimensional filters with applications to discretized diffusion processes
    • 21
    • PDF
    Predictive inference with Fleming--Viot-driven dependent Dirichlet processes
    • 2
    • PDF
    Conjugacy properties of time-evolving Dirichlet and gamma random measures
    • 6
    • PDF