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David Schnoerr
David Schnoerr
Data Scientist
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Title
Cited by
Cited by
Year
Approximation and inference methods for stochastic biochemical kinetics—a tutorial review
D Schnoerr, G Sanguinetti, R Grima
Journal of Physics A: Mathematical and Theoretical 50 (9), 093001, 2017
3442017
Comparison of different moment-closure approximations for stochastic chemical kinetics
D Schnoerr, G Sanguinetti, R Grima
The Journal of Chemical Physics 143 (18), 2015
1192015
The complex chemical Langevin equation
D Schnoerr, G Sanguinetti, R Grima
The Journal of chemical physics 141 (2), 2014
842014
A comprehensive network atlas reveals that Turing patterns are common but not robust
NS Scholes, D Schnoerr, M Isalan, MPH Stumpf
Cell systems 9 (3), 243-257. e4, 2019
782019
Validity conditions for moment closure approximations in stochastic chemical kinetics
D Schnoerr, G Sanguinetti, R Grima
The Journal of chemical physics 141 (8), 2014
702014
Exactly solvable models of stochastic gene expression
L Ham, D Schnoerr, RD Brackston, MPH Stumpf
The Journal of Chemical Physics 152 (14), 2020
402020
Cox process representation and inference for stochastic reaction–diffusion processes
D Schnoerr, R Grima, G Sanguinetti
Nature communications 7 (1), 11729, 2016
352016
Turing pattern design principles and their robustness
ST Vittadello, T Leyshon, D Schnoerr, MPH Stumpf
Philosophical Transactions of the Royal Society A 379 (2213), 20200272, 2021
312021
Expectation propagation for continuous time stochastic processes
B Cseke, D Schnoerr, M Opper, G Sanguinetti
Journal of Physics A: Mathematical and Theoretical 49 (49), 494002, 2016
19*2016
Efficient low-order approximation of first-passage time distributions
D Schnoerr, B Cseke, R Grima, G Sanguinetti
Physical review letters 119 (21), 210601, 2017
182017
Error estimates and specification parameters for functional renormalization
D Schnoerr, I Boettcher, JM Pawlowski, C Wetterich
Annals of Physics 334, 83-99, 2013
172013
Time-dependent product-form Poisson distributions for reaction networks with higher order complexes
DF Anderson, D Schnoerr, C Yuan
Journal of Mathematical Biology 80, 1919-1951, 2020
112020
Neural field models for latent state inference: Application to large-scale neuronal recordings
ME Rule, D Schnoerr, MH Hennig, G Sanguinetti
PLoS computational biology 15 (11), e1007442, 2019
92019
Probabilistic model checking for continuous-time Markov chains via sequential Bayesian inference
D Milios, G Sanguinetti, D Schnoerr
Quantitative Evaluation of Systems: 15th International Conference, QEST 2018 …, 2018
92018
The design principles of discrete Turing patterning systems
T Leyshon, E Tonello, D Schnoerr, H Siebert, MPH Stumpf
Journal of Theoretical Biology 531, 110901, 2021
72021
An alternative route to the system-size expansion
C Cianci, D Schnoerr, A Piehler, R Grima
Journal of Physics A: Mathematical and Theoretical 50 (39), 395003, 2017
62017
Probabilistic model checking for continuous time markov chains via sequential bayesian inference
D Milios, G Sanguinetti, D Schnoerr
arXiv preprint arXiv:1711.01863, 2017
52017
Learning system parameters from turing patterns
D Schnörr, C Schnörr
Machine Learning 112 (9), 3151-3190, 2023
42023
Turing patterns are common but not robust
NS Scholes, D Schnoerr, M Isalan, M Stumpf
bioRxiv, 352302, 2018
22018
Approximation methods and inference for stochastic biochemical kinetics
DB Schnoerr
The University of Edinburgh, 2016
12016
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