We lifted 3 functions out of this paper's own repositories and ran 3 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| SimOpt-FDU/PySOBA | pwc_unofficial | 3 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| Normal | Ran | SimOpt-FDU/PySOBA/Sampler.py code served (permissive licence) · get_code("62c3ff6c14363447") |
| SAN | Ran | SimOpt-FDU/PySOBA/Sampler.py code served (permissive licence) · get_code("30549ce10ea3b58a") |
| sSInvt | Ran | SimOpt-FDU/PySOBA/Sampler.py code served (permissive licence) · get_code("93e915c488f0dfec") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
In this paper, we briefly review the development of ranking-and-selection (R&S) in the past 70 years, especially the theoretical achievements and practical applications in the last 20 years. Different from the frequentist and Bayesian classifications adopted by Kim and Nelson (2006b) and Chick (2006) in their review articles, we categorize existing R&S procedures into fixed-precision and fixed-budget procedures, as in Hunter and Nelson (2017). We show that these two categories of procedures essentially differ in the underlying methodological formulations, i.e., they are built on hypothesis testing and dynamic-programming, respectively. In light of this variation, we review in detail some well-known procedures in the literature and show how they fit into these two formulations. In addition, we discuss the use of R&S procedures in solving various practical problems and propose what we think are the important research questions in the field.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2008.00249")
get_code_for_paper("2008.00249")
have("2008.00249")
Connect an agent — have() is free.