Showing items with similar title or keywords.

• Statistical analysis of life sequence data 

  Helske, Satu (University of Jyväskylä, 2016)

• 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 ...

• Optimization of Linearized Belief Propagation for Distributed Detection 

  Abdi, Younes; Ristaniemi, Tapani (IEEE, 2020)

  In this paper, we investigate distributed inference schemes, over binary-valued Markov random fields, which are realized by the belief propagation (BP) algorithm. We first show that a decision variable obtained by the BP ...

• 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 ...

• A comparison of joint species distribution models for percent cover data 

  Korhonen, Pekka; Hui, Francis K. C.; Niku, Jenni; Taskinen, Sara; van der Veen, Bert (Wiley, 2024)

  Joint species distribution models (JSDMs) have gained considerable traction among ecologists over the past decade, due to their capacity to answer a wide range of questions at both the species- and the community-level. The ...