Transcription of Musical Audio Using Poisson Point Processes and Sequential MCMC. - Pete Bunch; Simon J. Godsill (CMMR, 2010)

Point process MCMC for sequential music transcription. - Pete Bunch; Simon J. Godsill (ICASSP, 2011)

Dynamical models for tracking with the variable rate particle filter. - Pete Bunch; Simon J. Godsill (FUSION, 2012)

Particle filtering with progressive Gaussian approximations to the optimal importance density. - Pete Bunch; Simon J. Godsill (CAMSAP, 2013)

Rao-Blackwellized particle smoothers for mixed linear/nonlinear state-space models. - Fredrik Lindsten; Pete Bunch; Simon J. Godsill; Thomas B. Schön (ICASSP, 2013)

Improved Particle Approximations to the Joint Smoothing Distribution Using Markov Chain Monte Carlo. - Pete Bunch; Simon J. Godsill (IEEE Trans. Signal Process., 2013)

Particle Smoothing Algorithms for Variable Rate Models. - Pete Bunch; Simon J. Godsill (IEEE Trans. Signal Process., 2013)

Probabilistic Intentionality Prediction for Target Selection Based on Partial Cursor Tracks. - Bashar I. Ahmad; Patrick M. Langdon; Pete Bunch; Simon J. Godsill (HCI (6), 2014)

Particle Gibbs with refreshed backward simulation. - Pete Bunch; Fredrik Lindsten; Sumeetpal S. Singh (ICASSP, 2015)

Annealed MCMC for Bayesian learning of linear Gaussian state space models with changepoints. - Pete Bunch; James K. Murphy; Simon J. Godsill (FUSION, 2016)

Sparse structure inference for group and network tracking. - James K. Murphy; Emre Özkan; Pete Bunch; Simon J. Godsill (FUSION, 2016)

Rao-Blackwellized Particle Smoothers for Conditionally Linear Gaussian Models. - Fredrik Lindsten; Pete Bunch; Simo Särkkä; Thomas B. Schön; Simon J. Godsill (IEEE J. Sel. Top. Signal Process., 2016)

Bayesian Learning of Degenerate Linear Gaussian State Space Models Using Markov Chain Monte Carlo. - Pete Bunch; James K. Murphy; Simon J. Godsill (IEEE Trans. Signal Process., 2016)