Monte Carlo Methods
By J. Hammersley, D. Handscomb. 1964
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Citations in the corpus
- Offline A/B Testing for Recommender Systems (2018, background): “come anoffline A/B test. This setting is called counterfactual reasoning or off-policy evaluation (OPE) (see [2] for a comprehensive study). Several estimators such as Basic Importance Sampling (BIS, [8, 11]), Capped Importance Sampling (CIS, [2]), Normalised Importance Sampling (NIS, [21]) and Doub”
- Demystifying Double Robustness: A Comparison of Alternative Strategies for Estimating a Population Mean from Incomplete Data (2007, methodology): “The same idea is used in importance sampling, a Monte Carlo technique for approximating the moments of a distribution using random draws from another distribution that approximates it (Hammersley and Handscomb, 1964; Geweke, 1989).”
- The EGS5 code system (2005, methodology): “Halmos [69], Hammersley and Handscomb [70], Kingman and Taylor [89], Parzen[130], Loeve[99], Shreider[156], Spanier and Gelbard[157], Carter and Cashwell[41]) and we shall not try to duplicate their effort here.”
- The Theory and Practice of Simulated Annealing (2003, background): “Simulated annealing and the homogeneous convergence theory are based on the work of Metropolis et al. (1953), which addresses problems in equilibrium statistical mechanics (Hammersley and Handscomb, 1964).”
- Efficient high-dimensional importance sampling (2007, background): “in Kahn and Marshall (1953), Trotter and Tukey (1956) or Hammersley and Handscomb (1964,”
- Local Adaptive Importance Sampling for Multivariate Densities with Strong Nonlinear Relationships (1996, background): “…can be evaluated by assessing how much F p differs from Ft. Define as a measure of the distance from F p to Ft. Integration by parts yields D = var(w), which is recommended as a performance measure by Hammersley and Handscomb (1964), and D + 1 = IEt(!i)w(B), which is suggested by Geweke (1989 ).”