Stochastic modelling of protein folding and likelihood-free statistical inference methods

Picchini, Umberto (PI)
Forman, Julie (Assistant supervisor)
Lindorff-Larsen, Kresten (Assistant supervisor)
Wiqvist, Samuel (Research student)

Mathematical Statistics

University of Copenhagen

Project: Research

5 Article
2 Working paper

5 results

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2019
Bayesian inference for stochastic differential equation mixed effects models of a tumour xenography study
Picchini, U. & Forman, J. L., 2019 Aug, In: Journal of the Royal Statistical Society. Series C: Applied Statistics. 68, 4, p. 887-913 27 p.
Research output: Contribution to journal › Article › peer-review
Likelihood-free stochastic approximation EM for inference in complex models
Picchini, U., 2019, In: Communications in Statistics: Simulation and Computation. 48, 3, p. 861-881 26 p.
Research output: Contribution to journal › Article › peer-review
2018
Coupling stochastic EM and approximate Bayesian computation for parameter inference in state-space models
Picchini, U. & Samson, A., 2018 Mar, In: Computational Statistics. 33, 1, p. 179-212
Research output: Contribution to journal › Article › peer-review

Open Access
2017
Approximate maximum likelihood estimation using data-cloning ABC
Picchini, U. & Anderson, R., 2017, In: Computational Statistics & Data Analysis. 105, p. 166-183 18 p.
Research output: Contribution to journal › Article › peer-review
2016
Accelerating inference for diffusions observed with measurement error and large sample sizes using approximate Bayesian computation
Picchini, U. & Forman, J. L., 2016, In: Journal of Statistical Computation and Simulation. 86, 1, p. 195-213
Research output: Contribution to journal › Article › peer-review