Showing items with similar title or keywords.

• The Max-Product Algorithm Viewed as Linear Data-Fusion : A Distributed Detection Scenario 

  Abdi, Younes; Ristaniemi, Tapani (Institute of Electrical and Electronics Engineers (IEEE), 2020)

  In this paper, we disclose the statistical behavior of the max-product algorithm configured to solve a maximum a posteriori (MAP) estimation problem in a network of distributed agents. Specifically, we first build a ...

• On the use of approximate Bayesian computation Markov chain Monte Carlo with inflated tolerance and post-correction 

  Vihola, Matti; Franks, Jordan (Oxford University Press, 2020)

  Approximate Bayesian computation enables inference for complicated probabilistic models with intractable likelihoods using model simulations. The Markov chain Monte Carlo implementation of approximate Bayesian computation ...

• Conditional particle filters with diffuse initial distributions 

  Karppinen, Santeri; Vihola, Matti (Springer, 2021)

  Conditional particle filters (CPFs) are powerful smoothing algorithms for general nonlinear/non-Gaussian hidden Markov models. However, CPFs can be inefficient or difficult to apply with diffuse initial distributions, which ...

• Conditional particle filters with bridge backward sampling 

  Karppinen, Santeri; Singh, Sumeetpal S.; Vihola, Matti (Taylor & Francis, 2024)

  Conditional particle filters (CPFs) with backward/ancestor sampling are powerful methods for sampling from the posterior distribution of the latent states of a dynamic model such as a hidden Markov model. However, the ...

• The algorithmic nature of song-sequencing : statistical regularities in music albums 

  Neto, Pedro A. S. O.; Hartmann, Martin; Luck, Geoff; Toiviainen, Petri (Taylor & Francis, Informa, 2024)

  Based on a review of anecdotal beliefs, we explored statistical patterns of track-sequencing within a large set of released music albums. We found that songs with high levels of valence, energy and loudness are more likely ...