Archive for Beamer

\STATE [algorithmic package]

Posted in Books, Kids, pictures, R, Statistics, Travel, University life with tags , , , , , , , , , on June 8, 2012 by xi'an

I fought with my LαTεX compiler this morning as it did not want to deal with my code:

[sourcecode language=”latex” gutter=”false”]
\begin{algorithmic}[1]
\STATE N=1000
\STATE $\hat\pi=0$
\FOR {I=1,N}
\STATE X=RDN(1), Y=RDN(1)
\IF {$\text{X}^2+\text{Y}^2<1$}
$\hat\pi$ = $\hat\pi +1$
\ENDIF
\ENDFOR
\RETURN 4*$\hat\pi/$N
\end{algorithmic}
[/sourcecode]

looking on forums for incompatibilities between beamer and algorithmic, and adding all kinds of packages, to no avail. Until I realised one \STATE was missing:

[sourcecode language=”latex” gutter=”false”]
\begin{algorithmic}[1]
\STATE N=1000
\STATE $\hat\pi=0$
\FOR {I=1,N}
\STATE X=RDN(1), Y=RDN(1)
\IF {$\text{X}^2+\text{Y}^2<1$}
\STATE $\hat\pi$ = $\hat\pi +1$
\ENDIF
\ENDFOR
\RETURN 4*$\hat\pi/$N
\end{algorithmic}
[/sourcecode]

(This is connected with my AMSI public lecture on simulation, obviously!)

15 all-timers [back]

Posted in Statistics with tags , , , , , on November 26, 2010 by xi'an

Following an earlier post and poll. six of my graduate students took the Reading Classics seminar this year (plus two who dropped out). They chose

  1. W.K.Hastings (1970) Monte Carlo sampling methods using Markov chains and their applications, Biometrika
  2. G. Casella & W. Strawderman (1981) Estimation of a bounded mean Annals of Statistics
  3. A.P. Dawid, M. Stone & J. Zidek (1973) Marginalisation paradoxes in Bayesian and structural inference J. Royal Statistical Society
  4. C. Stein (1981) Estimation of the mean of a multivariate normal distribution Annals of Statistics
  5. D.V. Lindley & A.F.M. Smith (1972) Bayes Estimates for the Linear Model  J. Royal Statistical Society
  6. A. Birnbaum (1962) On the Foundations of Statistical Inference J. American Statistical Assoc.

in this order and mostly managed to grasp the quintessentials of the papers and to give decent (Beamer) presentations. The hardest one was the exposition of the likelihood principle and the student who chose this paper struggled to go past a mere repetition of the proofs. I enjoyed it nonetheless because the presentation raised questions about this principle,

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