22 citations · 23 across the 5 of their papers we have counts for
3 papers · 1 filter
Hess-MC2: Sequential Monte Carlo Squared using Hessian Information and Second Order Proposals
Joshua Murphy, Conor Rosato, Andrew Millard +3
When performing Bayesian inference using Sequential Monte Carlo (SMC) methods, two considerations arise: the accuracy of the posterior approximation and computational efficiency. T…
Enhanced SMC: Leveraging Gradient Information from Differentiable Particle Filters Within Langevin Proposals
Conor Rosato, Joshua Murphy, Alessandro Varsi +2
Sequential Monte Carlo Squared (SMC) is a Bayesian method which can infer the states and parameters of non-linear, non-Gaussian state-space models. The standard random-walk pro…
Efficient Learning of the Parameters of Non-Linear Models using Differentiable Resampling in Particle Filters
Conor Rosato, Vincent Beraud, Paul Horridge +2
It has been widely documented that the sampling and resampling steps in particle filters cannot be differentiated. The {\itshape reparameterisation trick} was introduced to allow t…