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University of New Mexico

Card counting meets hidden Markov models

Abstract

dc:description.abstract

The Hidden Markov Model (HMM) is a stochastic process that involves an unobservable Markov Chain and an observable output at each state in the chain. Hidden Markov Models are described by three parameters: A, B, and \uf070. A is a matrix that holds the transition probabilities for the unobservable states. B is a matrix that holds the probabilities for the output of an observable event at each unobservable state. Finally, \uf070 represents the prior probability of beginning in a particular unobservable state. Three fundamental questions arise with respect to HMMs. First, given A, B, and \uf070, what is the probability a specific observation sequence will be seen? Second, given A, B, \uf070 and an observation sequence, what is the most probable sequence of hidden states that produced the output? Finally, given a set of training data, estimate A, B, and \uf070. There are a number of tools that have been developed to answer these questions. Woolworth Blackjack is a variation of Blackjack played with a deck consisting of 20 fives and 32 tens. The object is to get a close to 20 as possible without going over. The player using a basic strategy loses to the dealer. The aim of this research is to develop a winning counting strategy for Woolworth Blackjack and then attempt to improve upon the counting strategy with a HMM using well-established HMM analysis tools. A secondary goal is to understand when to use counting strategies and when to use HMM's.'

Degree

thesis:*
Name thesis:degree_name
Electrical Engineering
Level thesis:degree_level
Thesis
Discipline thesis:degree_discipline
Electrical and Computer Engineering
Year
2011

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Aragon, Steven J.
Contributors dc:contributor
  • Jordan, Ramiro
  • Jayaweera, Sudharman
  • Solomon, Otis Jr.

Subjects

dc:subject × 3

Rights

Language dc:language
English

Identifiers

dc:identifier.*
Repository record dc:identifier
https://digitalrepository.unm.edu/ece_etds/17
OAI identifier oai:identifier
oai:digitalrepository.unm.edu:ece_etds-1016

Chain of custody

source
Harvested from
University of New Mexico
Base URL
digitalrepository.unm.edu/do/oai/
Last updated
2026-07-24
Source record
OAI-PMH GetRecord
citation

Aragon, Steven J.. Card counting meets hidden Markov models. Thesis thesis, 2011. https://digitalrepository.unm.edu/ece_etds/17