Postprocessing of MCMC von South, Leah F, Riabiz, Marina, Teymur, Onur, Oates, Chris J

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Revisiting Fishery Sustainability Targets von Cattoni, Vincent, South, Leah F., Warne, David J., Boettiger, Carl, Thakran, Bhavya, Holden, Matthew H.

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BSL : An R Package for Efficient Parameter Estimation for Simulation-Based Models via Bayesian Synthetic Likelihood von An, Ziwen, South, Leah F., Drovandi, Christopher

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CONTRIBUTIONS TO COMPUTATIONAL BAYESIAN STATISTICS von SOUTH, LEAH

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Efficient and generalizable tuning strategies for stochastic gradient MCMC von Coullon, Jeremie, South, Leah, Nemeth, Christopher

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Automatically adapting the number of state particles in SMC$$^2 von Botha, Imke, Kohn, Robert, South, Leah, Drovandi, Christopher

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Automatically adapting the number of state particles in SMC2 von Botha, Imke, Kohn, Robert, South, Leah, Drovandi, Christopher

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Nested sampling for physical scientists von Ashton, Greg, Bernstein, Noam, Buchner, Johannes, Chen, Xi, Csányi, Gábor, Fowlie, Andrew, Feroz, Farhan, Griffiths, Matthew, Handley, Will, Habeck, Michael, Higson, Edward, Hobson, Michael, Lasenby, Anthony, Parkinson, David, Pártay, Livia B., Pitkin, Matthew, Schneider, Doris, Speagle, Joshua S., South, Leah, Veitch, John, Wacker, Philipp, Wales, David J., Yallup, David

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Post-Processing of MCMC von South, Leah F, Riabiz, Marina, Teymur, Onur, Oates, Chris J

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Control Variates for MCMC von South, Leah, Sutton, Matthew

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Author Correction: Nested sampling for physical scientists von Ashton, Greg, Bernstein, Noam, Buchner, Johannes, Chen, Xi, Csányi, Gábor, Fowlie, Andrew, Feroz, Farhan, Griffiths, Matthew, Handley, Will, Habeck, Michael, Higson, Edward, Hobson, Michael, Lasenby, Anthony, Parkinson, David, Pártay, Livia B., Pitkin, Matthew, Schneider, Doris, Speagle, Joshua S., South, Leah, Veitch, John, Wacker, Philipp, Wales, David J., Yallup, David

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Nested Sampling for physical scientists von Ashton, Greg, Bernstein, Noam, Buchner, Johannes, Chen, Xi, Csányi, Gábor, Feroz, Farhan, Fowlie, Andrew, Griffiths, Matthew, Habeck, Michael, Handley, Will, Higson, Edward, Hobson, Michael, Lasenby, Anthony, Parkinson, David B., Pártay, Livia B., Pitkin, Matthew, Schneider, Doris, South, Leah, Speagle, Joshua, Veitch, John, Wacker, Philipp, Wales, David, Yallup, David

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The Polynomial Stein Discrepancy for Assessing Moment Convergence von Srinivasan, Narayan, Sutton, Matthew, Drovandi, Christopher, South, Leah F

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Control Variates for MCMC von South, Leah, Sutton, Matthew

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Dynamic Learning Rate for Deep Reinforcement Learning: A Bandit Approach von Donâncio, Henrique, Barrier, Antoine, South, Leah F, Forbes, Florence

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Count-Min Sketches for Estimating Password Frequency within Hamming Distance Two von South, Leah, Stebila, Douglas

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Adaptively switching between a particle marginal Metropolis-Hastings and a particle Gibbs kernel in SMC$^2 von Botha, Imke, Kohn, Robert, South, Leah, Drovandi, Christopher

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Efficient and Generalizable Tuning Strategies for Stochastic Gradient MCMC von Coullon, Jeremie, South, Leah, Nemeth, Christopher

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Automatically adapting the number of state particles in SMC$^2 von Botha, Imke, Kohn, Robert, South, Leah, Drovandi, Christopher

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BSL: An R Package for Efficient Parameter Estimation for Simulation-Based Models via Bayesian Synthetic Likelihood von An, Ziwen, South, Leah F, Drovandi, Christopher

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