Merge branch 'master' of raven.am28.uni-tuebingen.de:scientificComputing
This commit is contained in:
commit
088c2561da
@ -1,11 +1,11 @@
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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\chapter{\tr{Maximum likelihood estimation}{Maximum-Likelihood Methode}}
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\chapter{\tr{Maximum likelihood estimation}{Maximum-Likelihood-Sch\"atzer}}
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In vielen Situationen wollen wir einen oder mehrere Parameter $\theta$
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einer Wahrscheinlichkeitsverteilung sch\"atzen, so dass die Verteilung
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die Daten $x_1, x_2, \ldots x_n$ am besten beschreibt. Bei der
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Maximum-Likelihood-Methode w\"ahlen wir die Parameter so, dass die
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die Daten $x_1, x_2, \ldots x_n$ am besten beschreibt.
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Maximum-Likelihood-Sch\"atzer w\"ahlen wir die Parameter so, dass die
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Wahrscheinlichkeit, dass die Daten aus der Verteilung stammen, am
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gr\"o{\ss}ten ist.
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@ -89,7 +89,7 @@ nach dem Parameter $\theta$ und setzen diese gleich Null:
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\Leftrightarrow \quad n \theta & = & \sum_{i=1}^n x_i \\
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\Leftrightarrow \quad \theta & = & \frac{1}{n} \sum_{i=1}^n x_i
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\end{eqnarray*}
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Der Maximum-Likelihood-Estimator ist das arithmetische Mittel der Daten. D.h.
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Der Maximum-Likelihood-Sch\"atzer ist das arithmetische Mittel der Daten. D.h.
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das arithmetische Mittel maximiert die Wahrscheinlichkeit, dass die Daten aus einer
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Normalverteilung mit diesem Mittelwert gezogen worden sind.
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@ -106,7 +106,7 @@ Normalverteilung mit diesem Mittelwert gezogen worden sind.
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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\section{Kurvenfit als Maximum Likelihood Estimation}
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\section{Kurvenfit als Maximum-Likelihood Sch\"atzung}
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Beim Kurvenfit soll eine Funktion $f(x;\theta)$ mit den Parametern
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$\theta$ an die Datenpaare $(x_i|y_i)$ durch Anpassung der Parameter
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$\theta$ gefittet werden. Wenn wir annehmen, dass die $y_i$ um die
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@ -132,18 +132,22 @@ Maximum weggelassen werden.
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Anstatt nach dem Maximum zu suchen, k\"onnen wir auch das Vorzeichen der Log-Likelihood
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umdrehen und nach dem Minimum suchen. Dabei k\"onnen wir auch den Faktor $1/2$ vor der Summe vernachl\"assigen --- auch das \"andert nichts an der Position des Minimums.
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\begin{equation}
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\label{chisqmin}
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\theta_{mle} = \text{argmin}_{\theta} \; \sum_{i=1}^n \left( \frac{y_i-f(x_i;\theta)}{\sigma_i} \right)^2 \;\; = \;\; \text{argmin}_{\theta} \; \chi^2
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\end{equation}
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Die Summer der quadratischen Abst\"ande normiert auf die jeweiligen
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Die Summe der quadratischen Abst\"ande normiert auf die jeweiligen
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Standardabweichungen wird auch mit $\chi^2$ bezeichnet. Der Wert des
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Parameters $\theta$ welcher den quadratischen Abstand minimiert ist
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also identisch mit der Maximierung der Wahrscheinlichkeit, dass die
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Daten tats\"achlich aus der Funktion stammen k\"onnen. Minimierung des
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$\chi^2$ ist also ein Maximum-Likelihood Estimate.
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$\chi^2$ ist also eine Maximum-Likelihood Sch\"atzung. Aber nur, wenn
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die Daten normalverteilt um die Funktion streuen! Bei anderen
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Verteilungen m\"usste man die Log-Likelihood entsprechend
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\eqnref{loglikelihood} ausrechnen und maximieren.
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\begin{figure}[t]
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\includegraphics[width=1\textwidth]{mlepropline}
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\caption{\label{mleproplinefig} Maximum Likelihood Estimation der
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\caption{\label{mleproplinefig} Maximum-Likelihood Sch\"atzung der
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Steigung einer Ursprungsgeraden.}
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\end{figure}
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@ -186,12 +190,13 @@ Abstands an ein Histogram der Daten zu fitten. Das ist aber aus
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folgenden Gr\"unden nicht die Methode der Wahl: (i)
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Wahrscheinlichkeitsdichten k\"onnen nur positiv sein. Darum k\"onnen
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insbesondere bei kleinen Werten die Daten nicht symmetrisch streuen,
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wie es normalverteilte Daten machen sollten. (ii) Die Datenwerte sind
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nicht unabh\"angig, da das normierte Histogram sich zu Eins
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aufintegriert. Die beiden Annahmen normalverteilte und unabh\"angige Daten
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die die Minimierung des quadratischen Abstands zu einem Maximum
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Likelihood Estimator machen sind also verletzt. (iii) Das Histgramm
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h\"angt von der Wahl der Klassenbreite ab.
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wie es bei normalverteilte Daten der Fall ist. (ii) Die Datenwerte
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sind nicht unabh\"angig, da das normierte Histogram sich zu Eins
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aufintegriert. Die beiden Annahmen normalverteilte und unabh\"angige
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Daten, die die Minimierung des quadratischen Abstands
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\eqnref{chisqmin} zu einem Maximum-Likelihood Sch\"atzer machen, sind
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also verletzt. (iii) Das Histgramm h\"angt von der Wahl der
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Klassenbreite ab.
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Den direkten Weg, eine Wahrscheinlichkeitsdichtefunktion an ein
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Datenset zu fitten, haben wir oben schon bei dem Beispiel zur
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@ -204,9 +209,11 @@ z.B. dem Gradientenabstieg, gel\"ost wird.
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\begin{figure}[t]
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\includegraphics[width=1\textwidth]{mlepdf}
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\caption{\label{mlepdffig} Maximum Likelihood Estimation einer
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Wahrscheinlichkeitsdichtefunktion. Links: die 100 Datenpunkte, die aus der Gammaverteilung
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2. Ordnung (rot) gezogen worden sind. Der Maximum-Likelihood-Fit ist orange dargestellt.
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Rechts: das normierte Histogramm der Daten zusammen mit der \"uber Minimierung
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des quadratischen Abstands zum Histogramm berechneten Fits ist potentiell schlechter.}
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\caption{\label{mlepdffig} Maximum-Likelihood Sch\"atzung einer
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Wahrscheinlichkeitsdichtefunktion. Links: die 100 Datenpunkte, die
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aus der Gammaverteilung 2. Ordnung (rot) gezogen worden sind. Der
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Maximum-Likelihood-Fit ist orange dargestellt. Rechts: das
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normierte Histogramm der Daten zusammen mit der \"uber Minimierung
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des quadratischen Abstands zum Histogramm berechneten Fits ist
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potentiell schlechter.}
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\end{figure}
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|
225
pointprocesses/lecture/pointprocesses-chapter.tex
Normal file
225
pointprocesses/lecture/pointprocesses-chapter.tex
Normal file
@ -0,0 +1,225 @@
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\documentclass[12pt]{report}
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%%%%% title %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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\title{\tr{Introduction to Scientific Computing}{Einf\"uhrung in die wissenschaftliche Datenverarbeitung}}
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\author{Jan Benda\\Abteilung Neuroethologie\\[2ex]\includegraphics[width=0.3\textwidth]{UT_WBMW_Rot_RGB}}
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\date{WS 15/16}
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%%%% language %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% \newcommand{\tr}[2]{#1} % en
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% \usepackage[english]{babel}
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\newcommand{\tr}[2]{#2} % de
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\usepackage[german]{babel}
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%%%%% packages %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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\usepackage{pslatex} % nice font for pdf file
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\usepackage[breaklinks=true,bookmarks=true,bookmarksopen=true,pdfpagemode=UseNone,pdfstartview=FitH,colorlinks=true,citecolor=blue]{hyperref}
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%%%% layout %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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\usepackage[left=25mm,right=25mm,top=20mm,bottom=30mm]{geometry}
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\setcounter{tocdepth}{1}
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%%%%% section style %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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\usepackage[sf,bf,it,big,clearempty]{titlesec}
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\setcounter{secnumdepth}{1}
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%%%%% units %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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\usepackage[mediumspace,mediumqspace,Gray]{SIunits} % \ohm, \micro
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%%%%% figures %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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\usepackage{graphicx}
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\usepackage{xcolor}
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\pagecolor{white}
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\newcommand{\ruler}{\par\noindent\setlength{\unitlength}{1mm}\begin{picture}(0,6)%
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\put(0,4){\line(1,0){170}}%
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\multiput(0,2)(10,0){18}{\line(0,1){4}}%
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\multiput(0,3)(1,0){170}{\line(0,1){2}}%
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\put(0,0){\makebox(0,0){{\tiny 0}}}%
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\put(10,0){\makebox(0,0){{\tiny 1}}}%
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\put(20,0){\makebox(0,0){{\tiny 2}}}%
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\put(30,0){\makebox(0,0){{\tiny 3}}}%
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\put(40,0){\makebox(0,0){{\tiny 4}}}%
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\put(50,0){\makebox(0,0){{\tiny 5}}}%
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\put(60,0){\makebox(0,0){{\tiny 6}}}%
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\put(70,0){\makebox(0,0){{\tiny 7}}}%
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\put(80,0){\makebox(0,0){{\tiny 8}}}%
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\put(90,0){\makebox(0,0){{\tiny 9}}}%
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\put(100,0){\makebox(0,0){{\tiny 10}}}%
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\put(110,0){\makebox(0,0){{\tiny 11}}}%
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\put(120,0){\makebox(0,0){{\tiny 12}}}%
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\put(140,0){\makebox(0,0){{\tiny 14}}}%
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\put(150,0){\makebox(0,0){{\tiny 15}}}%
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\put(160,0){\makebox(0,0){{\tiny 16}}}%
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\put(170,0){\makebox(0,0){{\tiny 17}}}%
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\end{picture}\par}
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% figures:
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\setlength{\fboxsep}{0pt}
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\newcommand{\texpicture}[1]{{\sffamily\footnotesize\input{#1.tex}}}
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%\newcommand{\texpicture}[1]{\fbox{\sffamily\footnotesize\input{#1.tex}}}
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%\newcommand{\texpicture}[1]{\setlength{\fboxsep}{2mm}\fbox{#1}}
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%\newcommand{\texpicture}[1]{}
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\newcommand{\figlabel}[1]{\textsf{\textbf{\large \uppercase{#1}}}}
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% maximum number of floats:
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\setcounter{topnumber}{2}
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\setcounter{bottomnumber}{0}
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\setcounter{totalnumber}{2}
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% float placement fractions:
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\renewcommand{\textfraction}{0.2}
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\renewcommand{\topfraction}{0.8}
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\renewcommand{\bottomfraction}{0.0}
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\renewcommand{\floatpagefraction}{0.5}
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% spacing for floats:
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\setlength{\floatsep}{12pt plus 2pt minus 2pt}
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\setlength{\textfloatsep}{20pt plus 4pt minus 2pt}
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\setlength{\intextsep}{12pt plus 2pt minus 2pt}
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% spacing for a floating page:
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\makeatletter
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\setlength{\@fptop}{0pt}
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\setlength{\@fpsep}{8pt plus 2.0fil}
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\setlength{\@fpbot}{0pt plus 1.0fil}
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\makeatother
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% rules for floats:
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\newcommand{\topfigrule}{\vspace*{10pt}{\hrule height0.4pt}\vspace*{-10.4pt}}
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\newcommand{\bottomfigrule}{\vspace*{-10.4pt}{\hrule height0.4pt}\vspace*{10pt}}
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% captions:
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\usepackage[format=plain,singlelinecheck=off,labelfont=bf,font={small,sf}]{caption}
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% put caption on separate float:
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\newcommand{\breakfloat}{\end{figure}\begin{figure}[t]}
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% references to panels of a figure within the caption:
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\newcommand{\figitem}[1]{\textsf{\bfseries\uppercase{#1}}}
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% references to figures:
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\newcommand{\panel}[1]{\textsf{\uppercase{#1}}}
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\newcommand{\fref}[1]{\textup{\ref{#1}}}
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\newcommand{\subfref}[2]{\textup{\ref{#1}}\,\panel{#2}}
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% references to figures in normal text:
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\newcommand{\fig}{Fig.}
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\newcommand{\Fig}{Figure}
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\newcommand{\figs}{Figs.}
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\newcommand{\Figs}{Figures}
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\newcommand{\figref}[1]{\fig~\fref{#1}}
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\newcommand{\Figref}[1]{\Fig~\fref{#1}}
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\newcommand{\figsref}[1]{\figs~\fref{#1}}
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\newcommand{\Figsref}[1]{\Figs~\fref{#1}}
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\newcommand{\subfigref}[2]{\fig~\subfref{#1}{#2}}
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\newcommand{\Subfigref}[2]{\Fig~\subfref{#1}{#2}}
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\newcommand{\subfigsref}[2]{\figs~\subfref{#1}{#2}}
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\newcommand{\Subfigsref}[2]{\Figs~\subfref{#1}{#2}}
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% references to figures within bracketed text:
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\newcommand{\figb}{Fig.}
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\newcommand{\figsb}{Figs.}
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\newcommand{\figrefb}[1]{\figb~\fref{#1}}
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\newcommand{\figsrefb}[1]{\figsb~\fref{#1}}
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\newcommand{\subfigrefb}[2]{\figb~\subfref{#1}{#2}}
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\newcommand{\subfigsrefb}[2]{\figsb~\subfref{#1}{#2}}
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% references to tables:
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\newcommand{\tref}[1]{\textup{\ref{#1}}}
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% references to tables in normal text:
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\newcommand{\tab}{Tab.}
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\newcommand{\Tab}{Table}
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\newcommand{\tabs}{Tabs.}
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\newcommand{\Tabs}{Tables}
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\newcommand{\tabref}[1]{\tab~\tref{#1}}
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\newcommand{\Tabref}[1]{\Tab~\tref{#1}}
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\newcommand{\tabsref}[1]{\tabs~\tref{#1}}
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\newcommand{\Tabsref}[1]{\Tabs~\tref{#1}}
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% references to tables within bracketed text:
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\newcommand{\tabb}{Tab.}
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\newcommand{\tabsb}{Tab.}
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\newcommand{\tabrefb}[1]{\tabb~\tref{#1}}
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\newcommand{\tabsrefb}[1]{\tabsb~\tref{#1}}
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%%%%% equation references %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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%\newcommand{\eqref}[1]{(\ref{#1})}
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\newcommand{\eqn}{\tr{Eq}{Gl}.}
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\newcommand{\Eqn}{\tr{Eq}{Gl}.}
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\newcommand{\eqns}{\tr{Eqs}{Gln}.}
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\newcommand{\Eqns}{\tr{Eqs}{Gln}.}
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\newcommand{\eqnref}[1]{\eqn~\eqref{#1}}
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\newcommand{\Eqnref}[1]{\Eqn~\eqref{#1}}
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\newcommand{\eqnsref}[1]{\eqns~\eqref{#1}}
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\newcommand{\Eqnsref}[1]{\Eqns~\eqref{#1}}
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%%%%% listings %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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\usepackage{listings}
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\lstset{
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inputpath=../code,
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basicstyle=\ttfamily\footnotesize,
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numbers=left,
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showstringspaces=false,
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language=Matlab,
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commentstyle=\itshape\color{darkgray},
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keywordstyle=\color{blue},
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stringstyle=\color{green},
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backgroundcolor=\color{blue!10},
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breaklines=true,
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breakautoindent=true,
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columns=flexible,
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frame=single,
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caption={\protect\filename@parse{\lstname}\protect\filename@base},
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captionpos=t,
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xleftmargin=1em,
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xrightmargin=1em,
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aboveskip=10pt
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}
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%%%%% math stuff: %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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\usepackage{amsmath}
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\usepackage{bm}
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\usepackage{dsfont}
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\newcommand{\naZ}{\mathds{N}}
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\newcommand{\gaZ}{\mathds{Z}}
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\newcommand{\raZ}{\mathds{Q}}
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\newcommand{\reZ}{\mathds{R}}
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\newcommand{\reZpN}{\mathds{R^+_0}}
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\newcommand{\koZ}{\mathds{C}}
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%%%%% structure: %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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\usepackage{ifthen}
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\newcommand{\code}[1]{\texttt{#1}}
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\newcommand{\source}[1]{
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\begin{flushright}
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\color{gray}\scriptsize \url{#1}
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\end{flushright}
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}
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\newenvironment{definition}[1][]{\medskip\noindent\textbf{Definition}\ifthenelse{\equal{#1}{}}{}{ #1}:\newline}%
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{\medskip}
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\newcounter{maxexercise}
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\setcounter{maxexercise}{9} % show listings up to exercise maxexercise
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\newcounter{theexercise}
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\setcounter{theexercise}{1}
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\newenvironment{exercise}[1][]{\medskip\noindent\textbf{\tr{Exercise}{\"Ubung}
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\arabic{theexercise}:}\newline \newcommand{\exercisesource}{#1}}%
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{\ifthenelse{\equal{\exercisesource}{}}{}{\ifthenelse{\value{theexercise}>\value{maxexercise}}{}{\medskip\lstinputlisting{\exercisesource}}}\medskip\stepcounter{theexercise}}
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\graphicspath{{figures/}}
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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\begin{document}
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\include{pointprocesses}
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\end{document}
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@ -10,11 +10,14 @@
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werden kann. }
|
||||
\end{figure}
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||||
|
||||
Ein zeitlicher Punktprozess ist ein stochastischer Prozess der eine Abfolge von Ereignissen zu den Zeiten $\{t_i\}$, $t_i \in \reZ$ generiert.
|
||||
|
||||
Jeder Punktprozess wird durch einen sich in der Zeit kontinuierlichen
|
||||
entwickelnden Prozess generiert. Wann immer dieser Prozess eine Schwelle \"uberschreitet
|
||||
wird ein Ereigniss des Punktprozesses erzeugt. Zum Beispiel:
|
||||
Ein zeitlicher Punktprozess ist ein stochastischer Prozess, der eine
|
||||
Abfolge von Ereignissen zu den Zeiten $\{t_i\}$, $t_i \in \reZ$,
|
||||
generiert.
|
||||
|
||||
Jeder Punktprozess wird durch einen sich in der Zeit kontinuierlich
|
||||
entwickelnden Prozess generiert. Wann immer dieser Prozess eine
|
||||
Schwelle \"uberschreitet wird ein Ereigniss des Punktprozesses
|
||||
erzeugt. Zum Beispiel:
|
||||
\begin{itemize}
|
||||
\item Aktionspotentiale/Herzschlag: wird durch die Dynamik des
|
||||
Membranpotentials eines Neurons/Herzzelle erzeugt.
|
||||
@ -22,7 +25,7 @@ wird ein Ereigniss des Punktprozesses erzeugt. Zum Beispiel:
|
||||
tektonischen Platten auf beiden Seiten einer geologischen Verwerfung
|
||||
erzeugt.
|
||||
\item Zeitpunkt eines Grillen/Frosch/Vogelgesangs: wird durch die
|
||||
Dynamic des Nervensystems und des Muskelapparates erzeugt.
|
||||
Dynamik des Nervensystems und des Muskelapparates erzeugt.
|
||||
\end{itemize}
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||||
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||||
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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@ -1,7 +1,7 @@
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%!PS-Adobe-2.0 EPSF-2.0
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%%Title: pointprocessscetchA.tex
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%%Creator: gnuplot 4.6 patchlevel 4
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%%CreationDate: Sun Oct 25 21:47:09 2015
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%%CreationDate: Mon Oct 26 09:31:15 2015
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%%DocumentFonts:
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%%BoundingBox: 50 50 373 135
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%%EndComments
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@ -430,10 +430,10 @@ SDict begin [
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/Title (pointprocessscetchA.tex)
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/Subject (gnuplot plot)
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/Creator (gnuplot 4.6 patchlevel 4)
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/Author (jan)
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/Author (benda)
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% /Producer (gnuplot)
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% /Keywords ()
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/CreationDate (Sun Oct 25 21:47:09 2015)
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/CreationDate (Mon Oct 26 09:31:15 2015)
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/DOCINFO pdfmark
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end
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} ifelse
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@ -1,7 +1,7 @@
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%!PS-Adobe-2.0 EPSF-2.0
|
||||
%%Title: pointprocessscetchB.tex
|
||||
%%Creator: gnuplot 4.6 patchlevel 4
|
||||
%%CreationDate: Sun Oct 25 21:47:09 2015
|
||||
%%CreationDate: Mon Oct 26 09:31:16 2015
|
||||
%%DocumentFonts:
|
||||
%%BoundingBox: 50 50 373 237
|
||||
%%EndComments
|
||||
@ -430,10 +430,10 @@ SDict begin [
|
||||
/Title (pointprocessscetchB.tex)
|
||||
/Subject (gnuplot plot)
|
||||
/Creator (gnuplot 4.6 patchlevel 4)
|
||||
/Author (jan)
|
||||
/Author (benda)
|
||||
% /Producer (gnuplot)
|
||||
% /Keywords ()
|
||||
/CreationDate (Sun Oct 25 21:47:09 2015)
|
||||
/CreationDate (Mon Oct 26 09:31:16 2015)
|
||||
/DOCINFO pdfmark
|
||||
end
|
||||
} ifelse
|
||||
|
Binary file not shown.
@ -236,6 +236,6 @@
|
||||
|
||||
\renewcommand{\codepath}{pointprocesses/code/}
|
||||
\renewcommand{\texinputpath}{pointprocesses/lecture/}
|
||||
\include{pointprocesses/lecture/pointprocesses}
|
||||
%\include{pointprocesses/lecture/pointprocesses}
|
||||
|
||||
\end{document}
|
||||
|
@ -27,4 +27,4 @@ subplot(1, 2, 2);
|
||||
plot(psigs, loglm);
|
||||
xlabel('standard deviation')
|
||||
ylabel('log likelihood')
|
||||
savefigpdf(gcf, 'mlestd.pdf', 12, 5);
|
||||
savefigpdf(gcf, 'mlestd.pdf', 15, 5);
|
||||
|
Binary file not shown.
@ -113,8 +113,10 @@ Absch\"atzung der Standardabweichung verdeutlichen.
|
||||
\continue
|
||||
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
\question \qt{Maximum-Likelihood-Sch\"atzer einer Ursprungsgeraden}
|
||||
In der Vorlesung haben wir eine Gleichung f\"ur die Maximum-Likelihood
|
||||
Absch\"atzung der Steigung einer Ursprungsgeraden hergeleitet.
|
||||
In der Vorlesung haben wir folgende Formel f\"ur die Maximum-Likelihood
|
||||
Absch\"atzung der Steigung $\theta$ einer Ursprungsgeraden durch $n$ Datenpunkte $(x_i|y_i)$ mit Standardabweichung $\sigma_i$ hergeleitet:
|
||||
\[\theta = \frac{\sum_{i=1}^n \frac{x_iy_i}{\sigma_i^2}}{ \sum_{i=1}^n
|
||||
\frac{x_i^2}{\sigma_i^2}} \]
|
||||
\begin{parts}
|
||||
\part \label{mleslopefunc} Schreibe eine Funktion, die in einem $x$ und einem
|
||||
$y$ Vektor die Datenpaare \"uberreicht bekommt und die Steigung der
|
||||
@ -146,13 +148,12 @@ nicht so einfach wie der Mittelwert und die Standardabweichung einer
|
||||
Normalverteilung direkt aus den Daten berechnet werden k\"onnen. Solche Parameter
|
||||
m\"ussen dann aus den Daten mit der Maximum-Likelihood-Methode gefittet werden.
|
||||
|
||||
Um dies zu veranschaulichen ziehen wir uns diesmal Zufallszahlen, die nicht einer
|
||||
Normalverteilung entstammen, sonder aus der Gamma-Verteilung.
|
||||
Um dies zu veranschaulichen ziehen wir uns diesmal nicht normalverteilte Zufallszahlen, sondern Zufallszahlen aus der Gamma-Verteilung.
|
||||
\begin{parts}
|
||||
\part
|
||||
Finde heraus welche Funktion die Wahrscheinlichkeitsdichtefunktion
|
||||
(probability density function) der Gamma-Verteilung in \code{matlab}
|
||||
berechnet.
|
||||
Finde heraus welche \code{matlab} Funktion die
|
||||
Wahrscheinlichkeitsdichtefunktion (probability density function) der
|
||||
Gamma-Verteilung berechnet.
|
||||
|
||||
\part
|
||||
Plotte mit Hilfe dieser Funktion die Wahrscheinlichkeitsdichtefunktion
|
||||
@ -169,17 +170,17 @@ Normalverteilung entstammen, sonder aus der Gamma-Verteilung.
|
||||
|
||||
\part
|
||||
Finde heraus mit welcher \code{matlab}-Funktion eine beliebige
|
||||
Verteilung (``distribution'') und die Gammaverteilung an die
|
||||
Zufallszahlen nach der Maximum-Likelihood Methode gefittet werden
|
||||
kann.
|
||||
Verteilung (``distribution'') an die Zufallszahlen nach der
|
||||
Maximum-Likelihood Methode gefittet werden kann. Wie wird diese
|
||||
Funktion benutzt, um die Gammaverteilung an die Daten zu fitten?
|
||||
|
||||
\part
|
||||
Bestimme mit dieser Funktion die Parameter der
|
||||
Gammaverteilung aus den Zufallszahlen.
|
||||
Bestimme mit dieser Funktion die Parameter der Gammaverteilung aus
|
||||
den Zufallszahlen.
|
||||
|
||||
\part
|
||||
Plotte anschlie{\ss}end
|
||||
die Gammaverteilung mit den gefitteten Parametern.
|
||||
Plotte anschlie{\ss}end die Gammaverteilung mit den gefitteten
|
||||
Parametern.
|
||||
\end{parts}
|
||||
\begin{solution}
|
||||
\lstinputlisting{mlepdffit.m}
|
||||
|
133
statistics/lecture/statistics-slides.tex
Normal file
133
statistics/lecture/statistics-slides.tex
Normal file
@ -0,0 +1,133 @@
|
||||
\documentclass{beamer}
|
||||
|
||||
%%%%% title %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
\title[]{Scientific Computing --- Statistics}
|
||||
\author[]{Jan Benda}
|
||||
\institute[]{Neuroethology}
|
||||
\date[]{WS 14/15}
|
||||
\titlegraphic{\includegraphics[width=0.3\textwidth]{UT_WBMW_Rot_RGB}}
|
||||
|
||||
%%%%% beamer %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
\mode<presentation>
|
||||
{
|
||||
\usetheme{Singapore}
|
||||
\setbeamercovered{opaque}
|
||||
\usecolortheme{tuebingen}
|
||||
\setbeamertemplate{navigation symbols}{}
|
||||
\usefonttheme{default}
|
||||
\useoutertheme{infolines}
|
||||
% \useoutertheme{miniframes}
|
||||
}
|
||||
|
||||
%\AtBeginSection[]
|
||||
%{
|
||||
% \begin{frame}<beamer>
|
||||
% \begin{center}
|
||||
% \Huge \insertsectionhead
|
||||
% \end{center}
|
||||
% \end{frame}
|
||||
%}
|
||||
|
||||
\setbeamertemplate{blocks}[rounded][shadow=true]
|
||||
\setcounter{tocdepth}{1}
|
||||
|
||||
%%%%% packages %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
\usepackage[english]{babel}
|
||||
\usepackage{amsmath}
|
||||
\usepackage{bm}
|
||||
\usepackage{pslatex} % nice font for pdf file
|
||||
%\usepackage{multimedia}
|
||||
|
||||
\usepackage{dsfont}
|
||||
\newcommand{\naZ}{\mathds{N}}
|
||||
\newcommand{\gaZ}{\mathds{Z}}
|
||||
\newcommand{\raZ}{\mathds{Q}}
|
||||
\newcommand{\reZ}{\mathds{R}}
|
||||
\newcommand{\reZp}{\mathds{R^+}}
|
||||
\newcommand{\reZpN}{\mathds{R^+_0}}
|
||||
\newcommand{\koZ}{\mathds{C}}
|
||||
|
||||
%%%% graphics %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
\usepackage{graphicx}
|
||||
\newcommand{\texpicture}[1]{{\sffamily\small\input{#1.tex}}}
|
||||
|
||||
%%%%% listings %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
\usepackage{listings}
|
||||
\lstset{
|
||||
basicstyle=\ttfamily,
|
||||
numbers=left,
|
||||
showstringspaces=false,
|
||||
language=Matlab,
|
||||
commentstyle=\itshape\color{darkgray},
|
||||
keywordstyle=\color{blue},
|
||||
stringstyle=\color{green},
|
||||
backgroundcolor=\color{blue!10},
|
||||
breaklines=true,
|
||||
breakautoindent=true,
|
||||
columns=flexible,
|
||||
frame=single,
|
||||
captionpos=b,
|
||||
xleftmargin=1em,
|
||||
xrightmargin=1em,
|
||||
aboveskip=10pt
|
||||
}
|
||||
|
||||
\graphicspath{{figures/}}
|
||||
|
||||
|
||||
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
\begin{document}
|
||||
|
||||
\begin{frame}[plain]
|
||||
\frametitle{}
|
||||
\vspace{-1cm}
|
||||
\titlepage % erzeugt Titelseite
|
||||
\end{frame}
|
||||
|
||||
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
\begin{frame}
|
||||
\frametitle{Content}
|
||||
\tableofcontents
|
||||
\end{frame}
|
||||
|
||||
|
||||
\subsection{What is inferential statistics?}
|
||||
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
\begin{frame}
|
||||
\frametitle{sources of error in an experiment}
|
||||
\begin{task}{Think about it for 2 min}
|
||||
If you repeat a scientific experiment, why do you not get the same
|
||||
result every time you repeat it?
|
||||
\end{task}
|
||||
\pause
|
||||
\begin{itemize}
|
||||
\item sampling error (a finite subset of the population of interest
|
||||
is selected in each experiment)
|
||||
\item nonsampling errors (e.g. noise, uncontrolled factors)
|
||||
\end{itemize}
|
||||
\end{frame}
|
||||
|
||||
% ----------------------------------------------------------
|
||||
\begin{frame}[fragile]
|
||||
\frametitle{statisticians are lazy}
|
||||
\Large
|
||||
\only<1>{
|
||||
\begin{center}
|
||||
\includegraphics[width=.8\linewidth]{2012-10-29_16-26-05_771.jpg}
|
||||
\end{center}
|
||||
\mycite{Larry Gonick, The Cartoon Guide to Statistics}
|
||||
}\pause
|
||||
\only<2>{
|
||||
\begin{center}
|
||||
\includegraphics[width=.8\linewidth]{2012-10-29_16-41-39_523.jpg}
|
||||
\end{center}
|
||||
\mycite{Larry Gonick, The Cartoon Guide to Statistics}
|
||||
}\pause
|
||||
\only<3>{
|
||||
\begin{center}
|
||||
\includegraphics[width=.8\linewidth]{2012-10-29_16-29-35_312.jpg}
|
||||
\end{center}
|
||||
\mycite{Larry Gonick, The Cartoon Guide to Statistics}
|
||||
}
|
||||
\end{frame}
|
Reference in New Issue
Block a user