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Stein's method compares probability distributions through the study of a class of linear operators called Stein operators.
Variance reduction for MCMC methods via martingale representations
Belomestny, D., Moulines, E., Shagadatov, N., and Urusov, M. (2019) · 1903
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Stein variational gradient descent without gradient
Han, J. and Liu, Q. (2018) · 1908
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Random Feature Stein Discrepancies
Huggins, J. H. and Mackey, L. (2018) · 1909
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On the geometry of Stein variational gradient descent
Duncan, A., Nüsken, N., and Szpruch, L. (2019) · 1912
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Theory of reproducing kernels
Aronszajn, N. (1950) · 1950
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Inadmissibility of the usual estimator for the mean of a multivariate normal distribution
Stein, C. (1956) · 1956
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Estimation with quadratic loss
James, W. and Stein, C. (1961) · 1961
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Sous-espaces hilbertiens d’espaces vectoriels topologiques et noyaux associés (noyaux reproduisants)
Schwartz, L. (1964) · 1964
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A bound for the error in the normal approximation to the distribution of a sum of dependent random variables
Stein, C. (1972) · 1971
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Poisson approximation for dependent trials
Chen, L. H. (1975) · 1975
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On the rate of convergence in the central limit theorem for weakly dependent random variables
Tikhomirov, A. N. (1980) · 1980
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Estimation of the mean of a multivariate normal distribution
Stein, C. M. (1981) · 1981
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Zolotarev, V. M. (1984) · 1984
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On the consistency of Bayes estimates (with discussion and rejoinder by the authors)
Diaconis, P. and Freedman, D. (1986) · 1986
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Approximate computation of expectations
Stein, C. (1986) · 1986
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Stein’s method and Poisson process convergence
Barbour, A. D. (1988) · 1988
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Stein’s method for diffusion approximations
Barbour, A. D. (1990) · 1990
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A class of consistent tests for exponentiality based on the empirical Laplace transform
Baringhaus, L. and Henze, N. (1991) · 1991
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On the rate of convergence in the multivariate CLT
Götze, F. (1991) · 1991
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Poisson Approximation
Barbour, A. D., Holst, L., and Janson, S. (1992) · 1992
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A goodness of fit test for the Poisson distribution based on the empirical generating function
Baringhaus, L. and Henze, N. (1992) · 1992
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Variance reduction through smoothing and control variates for Markov chain simulations
Andradóttir, S., Heyman, D. P., and Ott, T. J. (1993) · 1993
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Integral probability metrics and their generating classes of functions
Müller, A. (1997) · 1997
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Couplings for normal approximations with Stein’s method
Reinert, G. (1998) · 1998
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Zero-variance principle for Monte Carlo algorithms
Assaraf, R. and Caffarel, M. (1999) · 1999
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Poisson perturbations
Barbour, A. D. and Xia, A. (1999) · 1999
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The reproducing Stein kernel approach for post-hoc corrected sampling
Hodgkinson, L., Salomone, R., and Roosta, F. (2020) · 2001
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On choosing and bounding probability metrics
Gibbs, A. L. and Su, F. E. (2002) · 2002
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Training products of experts by minimizing contrastive divergence
Hinton, G. E. (2002) · 2002
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Distributional regime for the number of
Lippert, R. A., Huang, H., and Waterman, M. S. (2002) · 2002
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Semi-exact control functionals from Sard’s method
South, L. F., Karvonen, T., Nemeth, C., Girolami, M., and Oates, C. (2020) · 2002
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Reproducing Kernel Hilbert Spaces in Probability and Statistics
Berlinet, A. and Thomas-Agnan, C. (2004) · 2004
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Stein’s Method: Expository Lectures and Applications
Diaconis, P. and Holmes, S., editors (2004) · 2004
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Relaxing the Gaussian assumption in shrinkage and SURE in high dimension
Fathi, M., Goldstein, L., Reinert, G., and Saumard, A. (2020) · 2004
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Adaptive simulation using perfect control variates
Henderson, S. G. and Simon, B. (2004) · 2004
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Stein’s method for birth and death chains
Holmes, S. (2004) · 2004
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Stein’s method for the bootstrap
Holmes, S. and Reinert, G. (2004) · 2004
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Use of exchangeable pairs in the analysis of simulations
Stein, C., Diaconis, P., Holmes, S., and Reinert, G. (2004) · 2004
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Distributional transformations, orthogonal polynomials, and Stein characterizations
Goldstein, L. and Reinert, G. (2005) · 2005
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Estimation of non-normalized statistical models by score matching
Hyvärinen, A. (2005) · 2005
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Three general approaches to stein’s method
Reinert, G. (2005) · 2005
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Optimal thinning of MCMC output
Riabiz, M., Chen, W., Cockayne, J., Swietach, P., Niederer, S. A., Mackey, L., and Oates, C. (2020) · 2005
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An explicit Berry–Esseen bound for Student’s
Shao, Q.-M. (2005) · 2005
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A kernel method for the two-sample-problem
Gretton, A., Borgwardt, K. M., Rasch, M., Schölkopf, B., and Smola, A. J. (2006) · 2006
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Scalable control variates for Monte Carlo methods via stochastic optimization
Si, S., Oates, C. J., Duncan, A. B., Carin, L., and Briol, F.-X. (2020) · 2006
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A Hilbert space embedding for distributions
Smola, A., Gretton, A., Song, L., and Schölkopf, B. (2007) · 2007
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On Stein’s identity and its applications
Kattumannil, S. K. (2009) · 2009
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Alignment-free sequence comparison (I): Statistics and power
Reinert, G., Chew, D., Sun, F., and Waterman, M. S. (2009) · 2009
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Approximation Theorems of Mathematical Statistics
Serfling, R. J. (2009) · 2009
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Vector valued reproducing kernel Hilbert spaces and universality
Carmeli, C., De Vito, E., Toigo, A., and Umanità, V. (2010) · 2010
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Normal Approximation by Stein’s method
Chen, L. H., Goldstein, L., and Shao, Q.-M. (2010) · 2010
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Stein couplings for normal approximation
Chen, L. H. and Röllin, A. (2010) · 2010
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Stein’s method, self-normalized limit theory and applications
Shao, Q.-M. (2010) · 2010
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Fundamentals of Stein’s method
Ross, N. (2011) · 2011
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Minimum probability flow learning
Sohl-Dickstein, J., Battaglino, P., and DeWeese, M. R. (2011) · 2011
Cited alongside, same era.
Bayesian learning via stochastic gradient langevin dynamics
Welling, M. and Teh, Y. W. (2011) · 2011
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Bayesian posterior sampling via stochastic gradient Fisher scoring
Ahn, S., Korattikara, A., and Welling, M. (2012) · 2012
Cited alongside, same era.
Control variates for estimation based on reversible Markov chain Monte Carlo samplers
Dellaportas, P. and Kontoyiannis, I. (2012) · 2012
Cited alongside, same era.
A kernel two-sample test
Gretton, A., Borgwardt, K. M., Rasch, M. J., Schölkopf, B., and Smola, A. (2012) · 2012
Cited alongside, same era.
Goodness-of-fit tests for the gamma distribution based on the empirical Laplace transform
Henze, N., Meintanis, S. G., and Ebner, B. (2012) · 2012
Goodness-of-fit testing for discrete distributions via Stein discrepancy
Yang, J., Liu, Q., Rao, V., and Neville, J. (2018) · 2018
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Bayesian deep convolutional encoder–decoder networks for surrogate modeling and uncertainty quantification
Zhu, Y. and Zabaras, N. (2018) · 2018
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Neural control variates for variance reduction
Zhu, Z., Wan, R., and Zhong, M. (2018) · 2018
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Message passing Stein variational gradient descent
Zhuo, J., Liu, C., Shi, J., Zhu, J., Chen, N., and Zhang, B. (2018) · 2018
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On Stein’s Method for Infinitely Divisible Laws with Finite First Moment
Arras, B. and Houdré, C. (2019) · 2019
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Minimum Stein discrepancy estimators
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Cited alongside, same era.
Markov Chains and Stochastic Stability
Meyn, S. P. and Tweedie, R. L. (2012) · 2012
Cited alongside, same era.
Normal Approximations with Malliavin Calculus: from Stein’s Method to Universality
Nourdin, I. and Peccati, G. (2012) · 2012
Cited alongside, same era.
Stein’s method for the beta distribution and the Pólya-Eggenberger urn
Goldstein, L. and Reinert, G. (2013) · 2013
Cited alongside, same era.
Dependent wild bootstrap for degenerate U- and V-statistics
Leucht, A. and Neumann, M. H. (2013) · 2013
Cited alongside, same era.
Zero variance Markov chain Monte Carlo for Bayesian estimators
Mira, A., Solgi, R., and Imparato, D. (2013) · 2013
Cited alongside, same era.
Stochastic Differential Equations: an Introduction with Applications
Oksendal, B. (2013) · 2013
Cited alongside, same era.
Barp, A. A., Briol, F.-X., Duncan, A. B., Girolami, M., and Mackey, L. (2019) · 2019
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A new characterization of the gamma distribution and associated goodness-of-fit tests
Betsch, S. and Ebner, B. (2019) · 2019
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Existence of Stein kernels under a spectral gap, and discrepancy bounds
Courtade, T. A., Fathi, M., and Pananjady, A. (2019) · 2019
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Multivariate approximations in Wasserstein distance by Stein’s method and Bismut’s formula
Fang, X., Shao, Q.-M., and Xu, L. (2019) · 2019
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Quantification of the impact of priors in Bayesian statistics via Stein’s method
Ghaderinezhad, F. and Ley, C. (2019) · 2019
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Quantile Stein Variational Gradient Descent for parallel Bayesian optimization
Gong, C., Peng, J., and Liu, Q. (2019) · 2019
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Measuring sample quality with diffusions
Gorham, J., Duncan, A. B., Vollmer, S. J., and Mackey, L. (2019) · 2019
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Scaling limit of the Stein Variational Gradient Descent: The mean field regime
Lu, J., Lu, Y., and Nolen, J. (2019) · 2019
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Convergence rates for a class of estimators based on Stein’s method
Oates, C. J., Cockayne, J., Briol, F.-X., and Girolami, M. (2019) · 2019
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Approximating stationary distributions of fast mixing Glauber dynamics, with applications to exponential random graphs
Reinert, G. and Ross, N. (2019) · 2019
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Nonlinear Stein variational gradient descent for learning diversified mixture models
Wang, D. and Liu, Q. (2019) · 2019
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Stein variational gradient descent with matrix-valued kernels
Wang, D., Tang, Z., Bajaj, C., and Liu, Q. (2019) · 2019
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A Stein–Papangelou goodness-of-fit test for point processes
Yang, J., Rao, V., and Neville, J. (2019) · 2019
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Seismic tomography using variational inference methods
Zhang, X. and Curtis, A. (2019) · 2019
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Bayesian semi-supervised learning for uncertainty-calibrated prediction of molecular properties and active learning
Zhang, Y. (2019) · 2019
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Bounds for the asymptotic distribution of the likelihood ratio
Anastasiou, A. and Reinert, G. (2020) · 2020
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The Bracket Geometry of Statistics
Barp, A. A. (2020) · 2020
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Variance reduction for Markov chains with application to MCMC
Belomestny, D., Iosipoi, L., Moulines, E., Naumov, A., and Samsonov, S. (2020) · 2020
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Testing normality via a distributional fixed point property in the Stein characterization
Betsch, S. and Ebner, B. (2020) · 2020
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SVGD as a kernelized Wasserstein gradient flow of the chi-squared divergence
Chewi, S., Gouic, T. L., Lu, C., Maunu, T., and Rigollet, P. (2020) · 2020
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Tests for multivariate normality – a critical review with emphasis on weighted
Ebner, B. and Henze, N. (2020) · 2020
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Kernelized Stein discrepancy tests of goodness-of-fit for time-to-event data
Fernández, T., Rivera, N., Xu, W., and Gretton, A. (2020) · 2020
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Stochastic Stein discrepancies
Gorham, J., Raj, A., and Mackey, L. (2020) · 2020
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Learning the Stein discrepancy for training and evaluating energy-based models without sampling
Grathwohl, W., Wang, K.-C., Jacobsen, J.-H., Duvenaud, D., and Zemel, R. (2020) · 2020
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Testing for normality in any dimension based on a partial differential equation involving the moment generating function
Henze, N. and Visagie, J. (2020) · 2020
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A non-asymptotic analysis for Stein variational gradient descent
Korba, A., Salim, A., Arbel, M., Luise, G., and Gretton, A. (2020) · 2020
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A stochastic version of Stein variational gradient descent for efficient sampling
Li, L., Li, Y., Liu, J.-G., Liu, Z., and Lu, J. (2020) · 2020
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Off-policy deep reinforcement learning with analogous disentangled exploration
Liu, A., Liang, Y., and Broeck, G. V. d. (2020) · 2020
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Variational full-waveform inversion
Zhang, X. and Curtis, A. (2020) · 2020
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Wasserstein distance error bounds for the multivariate normal approximation of the maximum likelihood estimator
Anastasiou, A. and Gaunt, R. E. (2021) · 2021
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A general framework for empirical Bayes estimation in discrete linear exponential family
Banerjee, T., Liu, Q., Mukherjee, G., and Sun, W. (2021) · 2021
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Fixed point characterizations of continuous univariate probability distributions and their applications
Betsch, S. and Ebner, B. (2021) · 2021
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A new test of multivariate normality by a double estimation in a characterizing PDE
Dörr, P., Ebner, B., and Henze, N. (2021) · 2021
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On combining the zero bias transform and the empirical characteristic function to test normality
Ebner, B. (2021) · 2021
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Measure transport with kernel Stein discrepancy
Fisher, M. A., Nolan, T. H., Graham, M. M., Prangle, D., and Oates, C. J. (2021) · 2021
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Bounds for the chi-square approximation of Friedman’s statistic by stein’s method
Gaunt, R. E. and Reinert, G. (2021) · 2021
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Sliced kernelized Stein discrepancy
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Composite goodness-of-fit tests with kernels
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Robust generalised Bayesian inference for intractable likelihoods
Matsubara, T., Knoblauch, J., Briol, F.-X., and Oates, C. J. (2021) · 2021
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Stein’s density method for multivariate continuous distributions
Mijoule, G., Reinert, G., and Swan, Y. (2021) · 2021
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Stein variational gradient descent: many-particle and long-time asymptotics
Nüsken, N. and Renger, D. (2021) · 2021
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Vector-valued control variates
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Optimal quantisation of probability measures using maximum mean discrepancy
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A Stein goodness-of-fit test for exponential random graph models
Xu, W. and Reinert, G. (2021) · 2021
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On testing the adequacy of the Inverse Gaussian distribution
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Characterizations of non-normalized discrete probability distributions and their application in statistics
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Bounds for the chi-square approximation of the power divergence family of statistics
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Generalised Bayesian inference for discrete intractable likelihood
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Standardisation-function kernel Stein discrepancy: A unifying view on kernel Stein discrepancy tests for goodness-of-fit
Xu, W. (2022) · 2022
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