scholarly journals Towards a Formal Treatment of Implicit Invocation Using Rely/Guarantee Reasoning

1998 ◽  
Vol 10 (3) ◽  
pp. 193-213 ◽  
Author(s):  
J. Dingel ◽  
D. Garlan ◽  
S. Jha ◽  
D. Notkin
Keyword(s):  
Formal Treatment

For a Formal Treatment of Premonition (VI) Some Conclusive Observations

2016 ◽  
Author(s):  
G. Arcolini ◽  
M. Caselli ◽  
Marco Maestro
Keyword(s):  
Formal Treatment

A Formal Treatment of the Pragmatics of Questions and Attitudes*

2005 ◽  
Vol 28 (5) ◽  
pp. 505-539 ◽  
Author(s):  
Maria Aloni
Keyword(s):  
Formal Treatment

A formal treatment of the causative constructions in Chinese

2008 ◽  
Vol 3 (2) ◽  
pp. 307-316
Author(s):  
Chongli Zou ◽  
Nianxi Xia
Keyword(s):  
Formal Treatment ◽  
Causative Constructions

scholarly journals A Formal Treatment of Context-Awareness

2004 ◽  
pp. 12-36 ◽  
Author(s):  
Gruia-Catalin Roman ◽  
Christine Julien ◽  
Jamie Payton
Keyword(s):  
Context Awareness ◽  
Formal Treatment

Markov Chain Monte Carlo

2015 ◽  
Author(s):  
N. Thompson Hobbs ◽  
Mevin B. Hooten
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Bayesian Analysis ◽  
Posterior Distribution ◽  
Mcmc Algorithm ◽  
Bayesian Analyses ◽  
Seminal Paper ◽  
Formal Treatment ◽  
High Level

This chapter explains how to implement Bayesian analyses using the Markov chain Monte Carlo (MCMC) algorithm, a set of methods for Bayesian analysis made popular by the seminal paper of Gelfand and Smith (1990). It begins with an explanation of MCMC with a heuristic, high-level treatment of the algorithm, describing its operation in simple terms with a minimum of formalism. In this first part, the chapter explains the algorithm so that all readers can gain an intuitive understanding of how to find the posterior distribution by sampling from it. Next, the chapter offers a somewhat more formal treatment of how MCMC is implemented mathematically. Finally, this chapter discusses implementation of Bayesian models via two routes—by using software and by writing one's own algorithm.

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