Teaching Markov Chain Monte Carlo: Revealing the Basic Ideas behind the Algorithm

Autor/inn/en	Stewart, Wayne; Stewart, Sepideh
Titel	Teaching Markov Chain Monte Carlo: Revealing the Basic Ideas behind the Algorithm
Quelle	In: PRIMUS, 24 (2014) 1, S.25-45 (21 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1051-1970
DOI	10.1080/10511970.2013.824054
Schlagwörter	Markov Processes; Monte Carlo Methods; College Mathematics; Mathematics Instruction; Simulation; Undergraduate Study; Bayesian Statistics; Probability; Learning Theories; Manipulative Materials; Teaching Methods; Foreign Countries; Australia + Suchen Sie Ihr Suchwort? Markowscher Prozess; Monte-Carlo-Methode; Mathematics lessons; Mathematikunterricht; Simulation program; Simulationsprogramm; Grundstudium; Wahrscheinlichkeitsrechnung; Wahrscheinlichkeitstheorie; Learning theory; Lerntheorie; Hilfsmittel; Teaching method; Lehrmethode; Unterrichtsmethode; Ausland; Australien
Abstract	For many scientists, researchers and students Markov chain Monte Carlo (MCMC) simulation is an important and necessary tool to perform Bayesian analyses. The simulation is often presented as a mathematical algorithm and then translated into an appropriate computer program. However, this can result in overlooking the fundamental and deeper conceptual ideas that are necessary for an effective diagnosis of MCMC output. In this paper we discuss MCMC simulation conceptually in the context of a Bayesian paradigm without revealing the formal algorithm first. We propose a tactile simulation method with a two-state discrete parameter where a coin supplies the proposal values and given the acceptance sets, the die value determines whether be not to accept the proposal. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10