A Doubly Stochastic Simulator with Applications in Arrivals Modeling and Simulation
References
- (2017) Wasserstein GAN. Preprint, submitted January 26, https://arxiv.org/abs/1701.07875.Google Scholar
- (2004) Modeling daily arrivals to a telephone call center. Management Sci. 50(7):896–908.Link, Google Scholar
- (2018) Approximability of discriminators implies diversity in GANs. Preprint, submitted June 27, https://arxiv.org/abs/1806.10586.Google Scholar
- (2005) Statistical analysis of a telephone call center: A queueing-science perspective. J. Amer. Statist. Assoc. 100(469):36–50.Crossref, Google Scholar
- (2020) NIM: Modeling and generation of simulation inputs via generative neural networks. 2020 Winter Simulation Conf. (IEEE, Piscataway, NJ).Google Scholar
- (2012) A normal copula model for the arrival process in a call center. Internat. Trans. Oper. Res. 19(6):771–787.Crossref, Google Scholar
- (2020) Statistical guarantees of generative adversarial networks for distribution estimation. Preprint, submitted February 20, https://arxiv.org/abs/2002.03938.Google Scholar
- (1954) On the superposition of renewal processes. Biometrika 41(1–2):91–99.Crossref, Google Scholar
- (2016) Recurrent marked temporal point processes: Embedding event history to vector. Proc. 22nd ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (ACM, New York), 1555–1564.Google Scholar
- (2014) Event labeling combining ensemble detectors and background knowledge. Progress Artificial Intelligence 2(2–3):113–127.Crossref, Google Scholar
- (2015) On the rate of convergence in Wasserstein distance of the empirical measure. Probab. Theory Related Fields 162(3):707–738.Crossref, Google Scholar
- (2014) Perspectives on Traffic Modeling, Markov Lecture, Applied Probability Society, Catonsville, MD.Google Scholar
- (2017) Improved training of Wasserstein GANs. Adv. Neural Inform. Processing Systems, 5767–5777.Google Scholar
- (2016) Modeling and forecasting call center arrivals: A literature survey and a case study. Internat. J. Forecasting 32(3):865–874.Crossref, Google Scholar
- (2016) Categorical reparameterization with Gumbel-Softmax. Preprint, submitted November 3, https://arxiv.org/abs/1611.01144.Google Scholar
- (2014) Choosing arrival process models for service systems: Tests of a nonhomogeneous Poisson process. Naval Res. Logist. 61(1):66–90.Crossref, Google Scholar
- (2014) Adam: A method for stochastic optimization. Proc. 3rd Internat. Conf. Learn. Representations (San Diego).Google Scholar
- (2013) Numerical Solution of Stochastic Differential Equations, vol. 23 (Springer Science & Business Media, Berlin).Google Scholar
- (2013) Call Center Optimisation (MG Books, Amsterdam).Google Scholar
- (1990) A unified view of the IPA, SF, and LR gradient estimation techniques. Management Sci. 36(11):1364–1383.Link, Google Scholar
- (1994) On the convergence rates of IPA and FDC derivative estimators. Oper. Res. 42(4):643–656.Link, Google Scholar
- (2018) Modeling bursts in the arrival process to an emergency call center. 2018 Winter Simulation Conf. (IEEE, Piscataway, NJ), 525–536.Google Scholar
- (1979) Simulation of nonhomogeneous Poisson processes by thinning. Nav. Res. Logist. Quart. 26(3):403–413.Crossref, Google Scholar
- (2016) The concrete distribution: A continuous relaxation of discrete random variables. Preprint, submitted November 2, https://arxiv.org/abs/1611.00712.Google Scholar
- (2017) The neural Hawkes process: A neurally self-modulating multivariate point process. Adv. Neural Inform. Processing Systems, 6754–6764.Google Scholar
- (2016) Some tactical problems in digital simulation for the next 10 years. J. Simulation 10(1):2–11.Crossref, Google Scholar
- (2016) Rate-based daily arrival process models with application to call centers. Oper. Res. 64(2):510–527.Link, Google Scholar
- (2011) On the absolute constants in the Berry-Esseen type inequalities for identically distributed summands. Preprint, submitted November 28, https://arxiv.org/abs/1111.6554.Google Scholar
- (2005) Performance measures for service systems with a random arrival rate. Proc. Winter Simulation Conf. (IEEE, Piscataway, NJ).Google Scholar
- (2009) Forecast errors in service systems. Probab. Engrg. Inform. Sci. 23(2):305–332.Crossref, Google Scholar
- (2022) Learning-based prediction of conditional wait time distributions in multiskill call centers. Proc. Internat. Conf. Oper. Res. Enterprise Systems (Springer, Berlin), 83–106.Google Scholar
- (1974) Calculation of the Wasserstein distance between probability distributions on the line. Theory Probab. Appl. 18(4):784–786.Crossref, Google Scholar
- (1991) Comparing alternative methods for derivative estimation when IPA does not apply directly. Nelson B, Kelton WD, Clark GM, eds. Proc. 1991 Winter Simulation Conf. Proc. (IEEE, Piscataway, NJ), 1004–1011.Google Scholar
- (2008) Optimal Transport: Old and New, vol. 338 (Springer Science & Business Media, Berlin).Google Scholar
- (2020) Estimating stochastic Poisson intensities using deep latent models. Preprint, submitted July 12, https://arxiv.org/abs/2007.06037.Google Scholar
- (2002) Stochastic-Process Limits: An Introduction to Stochastic-Process Limits and Their Application to Queues (Springer Science & Business Media, Berlin).Crossref, Google Scholar
- (2021) On optimally aggregating data when estimating the nonhomogeneous Poisson process rate function. Working paper, Purdue University, West Lafayette, IN.Google Scholar
- (2014) Scaling and modeling of call center arrivals. Proc. Winter Simulation Conf. (IEEE, Piscataway, NJ), 476–485.Google Scholar

