Mathematical analyses of casino rebate systems for VIP gambling
Bibliographic record
Abstract
In Macao, the VIP gaming revenue accounts for over two thirds of the total gaming revenue. Since the VIP gaming market is highly competitive, several incentives such as rebates are used to attract the VIP players. There are two commonly used rebate systems in the VIP gaming market: rebate on buy-in and rebate on actual loss. The analysis of rebate on buy-in is relatively easy. However, the analysis of rebate on actual loss is more complicated, which involves the Unit Normal Linear Loss Integral. Using empirical data, MacDonald derived a simple approximation formula for computing the rebate rate on actual loss. In this paper, we use mathematical analysis to derive more accurate approximation formulas for computing the rebate rate on actual loss. Some practical examples are given to compare the accuracies of these formulas. We also discuss how these two rebate systems affect the fluctuations of the results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".