Efficient Use of Randomisation Algorithms for Probability Prediction in Baccarat Using: Monte Carlo and Las Vegas Method

Abstract

Randomised algorithms use a degree of randomness in their logic. Such algorithms provide a solution which may not always be optimal, but is often produced faster than a brute force process. This paper explores the two classes of randomised algorithms: Las Vegas and Monte Carlo. A Las Vegas algorithm always produces the correct result, but its running time is based on a random value. A Monte Carlo algorithm has a deterministic running time and produces an answer that has a probability of ≥ 1/3 of being correct. By means of this paper, we develop algorithms that predict the winning probabilities of the betting options in the casino table card game of Baccarat. Both classes of algorithms have been implemented in two ways each, varying in space and time complexities. We also propose and implement a novel approach to reduce the time complexity of a typical Las Vegas algorithm through the use of multithreading. A comparative study for the algorithms has also been done.