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# Non Sequitur: An exploration of Python's random module

#### Automatisierte Medienanalyse

## Diese automatischen Videoanalysen setzt das TIB|AV-Portal ein:

**Szenenerkennung**—

**Shot Boundary Detection**segmentiert das Video anhand von Bildmerkmalen. Ein daraus erzeugtes visuelles Inhaltsverzeichnis gibt einen schnellen Überblick über den Inhalt des Videos und bietet einen zielgenauen Zugriff.

**Texterkennung**–

**Intelligent Character Recognition**erfasst, indexiert und macht geschriebene Sprache (zum Beispiel Text auf Folien) durchsuchbar.

**Spracherkennung**–

**Speech to Text**notiert die gesprochene Sprache im Video in Form eines Transkripts, das durchsuchbar ist.

**Bilderkennung**–

**Visual Concept Detection**indexiert das Bewegtbild mit fachspezifischen und fächerübergreifenden visuellen Konzepten (zum Beispiel Landschaft, Fassadendetail, technische Zeichnung, Computeranimation oder Vorlesung).

**Verschlagwortung**–

**Named Entity Recognition**beschreibt die einzelnen Videosegmente mit semantisch verknüpften Sachbegriffen. Synonyme oder Unterbegriffe von eingegebenen Suchbegriffen können dadurch automatisch mitgesucht werden, was die Treffermenge erweitert.

Erkannte Entitäten

Sprachtranskript

00:16

please welcome J is gonna tell that random module thank you non-sequitur an expression of fighters from but succeeded means it does not follow in Latin and actually this is name of talk because it's sort of describes the behavior of random sequences but also because my own interest in the topic and I was completely random and so if

00:49

it does not have much

00:50

practical relevance for me but still I think it's an interesting and beautiful topic in work talking about so I worker being Goffman's small development

01:04

shop in Mexico City and and I want to talk to you today about minimizing computers in the Python Standard Library so highlighting his work random because this

01:18

has its basically basic meanings of the unpredictability and personality idiosyncratic

01:24

annotation of 1st containing the of so this and in fact it

01:28

comes from Old French works that many things like speed of

01:31

violence a impulsiveness the Spanish word as I said that it comes from Arabic and 1st all dies so even now we called transients where this

01:45

had the mathematical term is very much related to to the gambling and meaning so it is not

01:54

coincidence that the 1st operations of probabilities In mathematical theory that measures analyzes and upon predicts random outcomes and has its roots on trying to understand gambling and what with listen into predicting deal from offending and 1 of the 1st examples of probabilistic analysis comes from a series of letters between Isaac Newton and some of that is the the president of Royal Society concerning some by and that some of it is was going to make that with the of the early in life as a process the animal the 1st thing that and we

02:34

hope that each of the 6 faces has an equal probability that come out when we're running as a before filling it with only know what this land government and even when we do a series of roles the information about the past outcomes of lies does not give us

02:50

any insight into what is going to come next the so if paradise and the number for comes out this for a

02:58

random number but it certainly isn't number chosen at random but just in looking at it we cannot know whether the process that produced it was actually a random so we can read randomness of individual numbers bad about

03:12

sequences of numbers and sequences of random numbers have many applications in real-world situations and the often used for reducing the size of a broadly based their by

03:24

sampling it at random points and this can be seen for instance in statistics where you take a

03:33

representative sample from publications like when you pick 2 people to call for my election polls or simulations word want approximated by the

03:43

sum of the probabilities of 4 fundamental properties and we can randomly generate events and then a statistical emission and the probability that we're looking for been relations a sequence of random numbers to be on a former distributed and I said this is

04:01

an even number in a certain range needs to come up with roughly equal probabilities for all the ways or associated intuitively at the same biases the 1st of this sequence and it's fair and reasonable uniform so they can consume the uses simulations of the source of numbers between 0 9 and in fact if we try and take the average the 1 of the the reasonably around for about 5 is what we will expect are for such a social

04:36

achievements and every number comes up roughly with the same frequency the but when the number of cells having more than that it been

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that means security and music you communication

04:51

algorithms that use random numbers for pseudonymization and so that all interested parties will notice this is a random number active graphics and schemes was used random number for teenagers signatures in a way that doesn't really informational became even if you have a lot of Signal messages and phrases in general there is still a lot of random and secret key that you need to use for every website that is used to seeing their sessions and in them so that users for that actors the temporal with them and thus reversing the scandal the inner but can be kept up there in a random number generator used by error state

05:38

in many of the products so apparently they can predict which random numbers the arity approach became which has

05:46

a sense of security implications so this cryptographic applications require must of time more than just an unbiased sequence of numbers the required this offensive of numbers to be actually predicted

06:00

and decadent knows which random numbers have Peking or even hassle inside and all do what what to look for the random

06:07

numbers and then behave as are open to all of my secrets so this is work about default it looks over maybe but it reasonable that the 1st piece

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of which of course complete the fix a

06:26

predictable sequence if another knew that I was using the from by for generating random numbers which you will only have to compute by and you will know all of my future begins so keeping in mind is

06:42

the requirements of impartiality and unpredictability that what can we use for getting pseudo sequences of random numbers 1 option is to use natural

06:52

phenomena that we know to be unpredictable when we show with sufficient accuracy instance elliptic random the this is that was very nice admission of food and this extracts random number from but in the UK there is a gene called but use transistor

07:13

measurements are the big winners in National lottery and we can also use radioactive isotopes the case which we know is the nature of predictable and

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independently independently of the position of all instruments or in the quality of our models but but disk is having gone is that it

07:34

is often the slow expensive and require all of specialist

07:41

equipment to measure and this sort of natural quantities to generate random numbers so it may be useful to generate this that a

07:50

large part of random numbers once and then compiling this into a table of random numbers that we can draw from an in the future as a matter of fact in 1955 at the RAND Corporation which a table of a million brand and the it's obtained from a specialized hardware and this knowledge of came to be widely used in simulations for engineering and science bad of course not Latin America they will still have some disadvantages of that especially with the computer from back then it is very characters and efficiently and access such a letter to last A which led researchers looking techniques and for random number generation on the fly so of course computers are deterministic effects so the future receipt of the

08:43

matching is completely determined by the present state was so how can that actually generate random numbers the what do so that others we incorporate input from outside devices but we can only generate pseudo-random numbers that is

09:01

to random number generator all the numbers that look around 1 statistically measuring them at but they're not actually hard to predict if you have enough information about the state of the United Internet importance number Newman was doing simulation work the a of stream of random numbers it he came up with a generating it by

09:27

taking a random number of squaring it and then taking the form the middle leads to over this next deal with the generated solution of random but it is

09:39

going to be corrected for it because for instance if we get a serial somewhere in there that means that from then on the sequences the land of and and it also has a tendency to avoid the the lord of the performing dilution look and which of course there's no way to getting out of if I we can use the received and make sure this how long this

10:09

generator run before starting to repeat numbers and we can see

10:13

that it for 40 years the sequences are not very long but if we take 1 of those long sequences and check the average value and the statistical properties we can see that they looked at regional around so this is what you will generally randomness more precisely

10:35

and if you want a mathematically valid randomness with ways to formalize what its impartiality and its unpredictability

10:42

aspects and as as I went from images predictability is to look at the end of each of oral i which is a measure of the space of possibilities that can take and it's important to note that this cannot be immediately tell from looking at the numbers you have you have to actually look in the process that generated for instance here we see those numbers and I told you that may be Democrat and directing the they pick them for arise from several

11:14

differ from 0 to 100 but what if I told you that they are actually prime numbers then you will see that the action space from which was brother was much smaller than without so it's you that whole my

11:28

bank as be a password but only can be like a correct long and they can use

11:34

repeating patterns representative numbers so generally a reducing in the space of possible possible so they can be

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of course it might be worth it it is if it stops people from using password of ranking better and extreme version of the we can look at as at the statistical

11:53

properties of a random sequence and see that they are consistent with the relativistic position predictions but when taking the randomness of the views of

12:03

5 we use the very informal tests for the rule of thumb which is that the average of the values was what was to be expected in random sequence and restraint and this this by looking at the low frequencies for different needs and see that the problem is but we have much elements to assess whether this is sufficiently rank of or distance from the world and it's only a little bit more quantitative at a much revelation is that she's corpus which is this of the

12:38

6 he's is set of that confirms the certain distributions but the general idea is to measure of the squares of the differences in reality but the values weighted by the expected value all and summing them so this

12:53

gives that sort of a measure of how much or observed frequencies deviates from what we would expect probabilistic early in the real world it with this measure we got what they were like this that he was the likelihood of observing different values of this

13:14

grammateme and if it is true do then what we we will conclude that the sequences this to the foreign or is too much deviated from but we will expect in a random sequence uh but also and different from the application of this text if it is too low then the the sequence of suspected to be you tool uniform for being granted other tests check this sequence from a complex patterns for instance in random sequences pairs of numbers need to be as uniformly distributed as numbers themselves or we can check if there gaps it and successive appearances of the same number and 1 of the length of that is consistent with a with with what we

14:12

would expect probabilistically and the social number of other problems and that we can use as a matter of fact the 1st and the and that

14:23

can be used to check at random sequences there's 1 by the American and I is the which of a series of formation of mathematical test as a series of programs that can isolate and a list of random numbers so can see more generators are random enough our on the other hand those some extended this led the miscegenation about

14:47

tests and that proved that this random numbers in some repeated regions like the spacing of embarrassing around random but relational or it tries to play circles and see which was overlap in the plane and many other our random experiments that we know what that's all what you expect and would it said that it is the performance of our random or some of the random sequence that taking into account the stress of randomness

15:20

but the generators have been devised 1 right the brazilian and

15:24

congressional generator which saw recurrence or would take initial value and use this equation to produce a chicken and of course is realized that I didn't have to repeat itself with a period no greater than n but it would be right used for a CNN we can and we can get a reasonably large sequences that exceed very good this this the coverage the growing with a set of them is that is very easy to fall into the situation all uniform numbers look

15:59

random once you when the block pairs of them based on that exhibits this kind of behavior and they're all falling and in the same straight lines we can choose better a to get rid of this behavior but it it always ends up happening at the dimension the so how do we get rid of it and the standard deviation from true random behavior 1 the most interesting is another and propose the maximum at somewhat undercutting Ishwaran which consists of a

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large linear fit useful dimension a way that permits as it

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was a very very large period of 2 to the 19 28 thousand but until more or less of it is also interesting that this sequence has internal state and uses that internal states to produce the actual random numbers so even if you know the

17:01

random number themselves and develop predicting immediately and the next number in the sequence you need a lot sample of him and and if you actually measure the statistical properties of the sequence but it it produces they are very very close randomness and and they exceeded or or this this we are correlations in in many dimensions of 633 dimensions so it is a very good random number generator what is the set of all

17:35

participants have made it a very popular generator this beginning in many languages and the Python is 1 of them but the by the random His messenger services its underlying default number of random random number generator there's also the question of how get to random numbers that people because you give this part of the company obtained from algorithm because I wouldn't have the music of so they have to be gathered from system activity only news and some other UNIX systems provide a source of random numbers in dev random and that is few by an entropy boom that that I've randomness

18:19

from various sources like keyword people sort of timing of mouth movements knowledge in the sound the memory the phrases such as and so when you have them to when users needs random numbers they can get through a random number from this book and of course getting random numbers so the poor sort of being beautiful rendering motion so

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when it directly usable with more and and besides the regular source of further assume a system like at the keyboard or the

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mouse and modern computer systems of them interoperate some form of hardware random number generation inductive stable bridge family a common dedicated random number generator in so now we have the right to the actual random Hamilton and the new

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patterns found in real it starts from

19:18

this generator of numbers it is your mind on a from retributive and provides a lot of other interesting distributions based on that so the way it is it is there is a class in the model random which can be seen

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as and and that provides a method random that's going to produce a sequence of numbers are from June 2 1 if we can use CD relevant to repeat with the same sequence all the users and

19:53

several times if we don't then we can't just let by them seeded with number get from that the random or from the number of seconds at the time of the call the have from real random numbers it is 0 when it is very

20:11

easy to get reality or in the year random numbers in all to a certain number we just multiply it around on wheels by the maximum value if you have a specific grants and you have to you random number to the width of the range and then after the by the start of this is still very is very easy and the emitted from the random and you also need to add a certain steps in this sequence of possible random numbers and user-generated media alter the number of steps and then after the way start and you have your intermediate so we can generate random real certain generated random Indians and if we need to include because of that that the whole interval numbers of so when within number of in a and B including a and B beginning is a special function branding that just false friend range with the appropriate documents but we can also be the awareness of a from celebrations of random operations in a sequence of phrases we might want to get a random element in the sequence which is very easy is generated in the in the range of indexes for the sequence a big and the element corresponding to that index if we the sample we use for the process several times if we want sample without replacement we need some form of tracking of which numbers we have already in a big as there are 2 ways to do it again tracks which numbers you can still think in least and remove from their marriage and a pigment or detainees assessed tool remember

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which number you have already peaked our the heart which is more efficient depends on the size of the population compared to the size of the sample and you wonder that and the by members will actually computes this on the fly and use the more efficient you also might want to shovel it had been used

22:25

by the random model is the future dates shuffle which is just the list and exchange every region with a randomly picked up for with another 1 of the

22:34

standard if we needed to these of course destroys the sequence of so even if you a simpler way to do it that gives us a new lease we can just sort by a random key this is not as efficient but but it's much more simple now we may be interested in front of the real numbers that have another distributions other than uniform 1 but how many

23:03

about it what let's consider the normal distribution and it is at the moment 2 parameters Music Lab and in this field because know which we have numbers we can know the probability of picking it up with this what is normal distribution and but we can also read the of the a random variable with this distribution falling before every real number and this is called the cumulative distributive and the cumulative distribution function for the Bible I would consider this always increase and this means that the success of normally distributed random numbers with then generate a uniform random number and all of that will

23:49

be the from that ways that we will use brother reading in this plot and then we can check to the 1 x those that probably the corresponds and therefore the that selection is going to be an number this does not only apply to the normal distribution with 2 solution for which we know distributed function

24:13

but but it is not always obvious or easy to generate the embarrassed collective distribution function just from looking at the distribution function so there are many mathematical objects that have been devised to use these combinations on so for instance or for the normal barrier this mission we use sort of

24:41

mathematical thinking what would be the random numbers them to generate a point in a typical and the

24:46

x and y coordinates of the point and end up being normally distributed and from there we can get a number of interesting distributions the that we might do you might know from science engineering

25:00

like the triangular distribution government distributions and the prior to the commission or do we want a solution to the spread of because it's so it can be a problem use of approximate the other ones the

25:14

another 1 of node is that when mice distribution there is sort of like a normal distribution but for angles measured because when we

25:22

have angles with incident several angles may actually correspond to the same point in this initiative so the efficient it's wrapped around the signal to consider the effect of this of dislike double and angles for every point and finally the random of great for the instance of random class and provide

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admittance s module methods so you can simple random and if you don't care about the state of the generator during those used the model functions that is units of like for affecting implications for because you need to independent generated for different experiments you can actually instance a glass and entered the manually it also subclass of the random class to write your random number generator and the debate and random model comes with it with here for but with compatibility the reasons and as an example of how how to provide your own random number generator and thus also see some random will and good numbers from the system provided a random number generator meaning in UNIX systems and this in a library that will connect to the random the dog 7 and use that as a source for random numbers so if you draw a random numbers you can use this and she is all of the other methods rely only on these generators in all 3 number from 2 the 1 and then they will

27:02

still work even if changing the source of the actual numbers so controlling the division of randomness is more philosophical than a mathematical problems that we can use mathematical definition is a very

27:17

useful for our proposed and active if you need sequences of the music but became as a friend and we can use to the random number generation but if we need number that completely unpredictable it sources event of a like input devices noise measurements or other external mental phenomena and for most of random number and it's by the grace more than adequate and finally will like to talk about the book that inspired his stuff this is a very good book the base very

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short basic program and users literally criticism techniques that

27:55

civil war it sounds for effective but is actually a very interesting book and a couple of randomness In this with cause the interesting interesting to this very very beautiful topic and the understanding of preventing wanted to off of this book is about

28:14

random numbers it is very juridical but it's also very funny the civilianize mathematics and and finally a joint to read a little bit more there's a series of really good articles of randomness in Dorothy baked that

28:30

might help you understand why is found this important in the the every good description the 2nd link there's a very good description of the whole of how statistical testing of random numbers and if you want to read more about the possible back there in the receives random number generator disaster began article very good so that will be

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interim

00:00

Arithmetischer Ausdruck

Folge <Mathematik>

Code

Randomisierung

Vorlesung/Konferenz

Lateinisches Quadrat

Modul

Quick-Sort

Computeranimation

Modul

00:46

Verzeichnisdienst

Randomisierung

Gruppoid

Computerunterstütztes Verfahren

Softwareentwickler

Computeranimation

Modul

01:18

Arithmetisches Mittel

Antwortfunktion

Vorhersagbarkeit

Vorlesung/Konferenz

Wort <Informatik>

01:43

Arithmetisches Mittel

Nichtlinearer Operator

Videospiel

Newton, Isaac

Prozess <Physik>

Mathematik

Reihe

Wurzel <Mathematik>

Term

Physikalische Theorie

Computeranimation

Analysis

02:33

Reihe

Zahlenbereich

Vorlesung/Konferenz

Information

Lie-Gruppe

Computeranimation

02:57

Folge <Mathematik>

Prozess <Physik>

Randomisierung

Zahlenbereich

Vorlesung/Konferenz

Kartesische Koordinaten

Computeranimation

Zufallsgenerator

Lesen <Datenverarbeitung>

03:24

Statistik

Folge <Mathematik>

Punkt

Gewichtete Summe

Kategorie <Mathematik>

Relativitätstheorie

Stichprobe

Ereignishorizont

Computeranimation

Zufallsgenerator

Diskrete Simulation

Stichprobenumfang

Wort <Informatik>

Simulation

Instantiierung

04:00

Folge <Mathematik>

Spannweite <Stochastik>

Digitalsignal

Euler-Winkel

Mittelwert

Diskrete Simulation

Uniforme Struktur

Zellularer Automat

Zahlenbereich

Quellcode

Frequenz

Computeranimation

04:46

Telekommunikation

Web Site

Anonymisierung

Computersicherheit

Nummerung

Elektronische Unterschrift

Zufallsgenerator

Algorithmus

Gruppe <Mathematik>

Randomisierung

Schlüsselverwaltung

Message-Passing

Aggregatzustand

Fehlermeldung

05:37

Folge <Mathematik>

Computersicherheit

Besprechung/Interview

Zahlenbereich

Vorlesung/Konferenz

Kartesische Koordinaten

Biprodukt

Zufallsgenerator

06:05

Folge <Mathematik>

Zahlenbereich

Vorlesung/Konferenz

Default

Computeranimation

Zufallsgenerator

06:41

Folge <Mathematik>

Ellipse

Vorhersagbarkeit

Vorlesung/Konferenz

Instantiierung

Konfiguration <Informatik>

Zufallsgenerator

07:11

Informationsmodellierung

Ortsoperator

Mini-Disc

Vorlesung/Konferenz

Einflussgröße

Computeranimation

07:40

Soundverarbeitung

Hardware

Diskrete Simulation

Natürliche Zahl

Mereologie

Vorlesung/Konferenz

Computer

Computerunterstütztes Verfahren

Quick-Sort

Computeranimation

Tabelle <Informatik>

Zufallsgenerator

08:42

Tabusuche

Dämon <Informatik>

Zahlenbereich

Ein-Ausgabe

Computeranimation

Zufallsgenerator

Streaming <Kommunikationstechnik>

Metropolitan area network

Uniforme Struktur

Vorlesung/Konferenz

Simulation

Information

Fréchet-Algebra

Pseudozufallszahlen

Aggregatzustand

09:26

Arithmetisch-logische Einheit

Metropolitan area network

Folge <Mathematik>

Bildschirmmaske

Datenerfassung

Randomisierung

Computeranimation

Verdünnung <Bildverarbeitung>

Instantiierung

Zufallsgenerator

10:08

Folge <Mathematik>

Mittelwert

Kategorie <Mathematik>

Randomisierung

Zahlenbereich

Fréchet-Algebra

Computeranimation

Gammafunktion

10:34

Prozess <Physik>

Prognoseverfahren

Validität

Randomisierung

Zahlenbereich

Vorhersagbarkeit

Vorlesung/Konferenz

Bildgebendes Verfahren

Einflussgröße

Raum-Zeit

Computeranimation

Instantiierung

11:11

Primzahl

Gruppenoperation

Familie <Mathematik>

Vorlesung/Konferenz

Passwort

Raum-Zeit

Computeranimation

11:34

Prognoseverfahren

Ortsoperator

Allgemeine Relativitätstheorie

Kategorie <Mathematik>

Mustersprache

Selbstrepräsentation

Randomisierung

Versionsverwaltung

Zahlenbereich

Vorlesung/Konferenz

Computeranimation

12:01

Softwaretest

Distributionstheorie

Bit

Folge <Mathematik>

Subtraktion

Thumbnail

Schlussregel

Element <Mathematik>

Computeranimation

Quadratzahl

Rangstatistik

Mittelwert

Randomisierung

Vorlesung/Konferenz

Abstand

12:53

Softwaretest

Dicke

Folge <Mathematik>

Likelihood-Funktion

Zahlenbereich

Kartesische Koordinaten

Frequenz

Komplex <Algebra>

Quick-Sort

Computeranimation

Metropolitan area network

Mustersprache

Uniforme Struktur

Randomisierung

Vorlesung/Konferenz

Einflussgröße

Instantiierung

14:11

Softwaretest

Metropolitan area network

Generator <Informatik>

Folge <Mathematik>

Mathematik

Reihe

Dateiformat

Zahlenbereich

Mailing-Liste

Optimierung

Computeranimation

Zufallsgenerator

14:47

Generator <Informatik>

Folge <Mathematik>

Rechter Winkel

ATM

Relativitätstheorie

Randomisierung

Vorlesung/Konferenz

Normalspannung

Dialekt

Computeranimation

Zufallsgenerator

15:23

Folge <Mathematik>

Extrempunkt

Hausdorff-Dimension

Zahlenbereich

Anfangswertproblem

Gleichungssystem

p-Block

Frequenz

Computeranimation

Differenzengleichung

Metropolitan area network

Menge

Randomisierung

Fréchet-Algebra

Gerade

Standardabweichung

16:35

Folge <Mathematik>

Lineare Regression

Hausdorff-Dimension

Bildschirmsymbol

Frequenz

Computeranimation

Aggregatzustand

Zufallsgenerator

17:01

Folge <Mathematik>

Menge

Kategorie <Mathematik>

Hausdorff-Dimension

Stichprobenumfang

Güte der Anpassung

Zahlenbereich

Vorlesung/Konferenz

Korrelationsfunktion

Zufallsgenerator

17:35

Formale Sprache

Baum <Mathematik>

Zahlenbereich

Oval

Physikalisches System

Quellcode

Quick-Sort

Computeranimation

Zufallsgenerator

Metropolitan area network

Dienst <Informatik>

Algorithmus

Code

Festspeicher

Mereologie

Randomisierung

Entropie

Default

Fréchet-Algebra

Modul

18:42

Schnelltaste

Bildschirmmaske

Stabilitätstheorie <Logik>

Hardware

Statistische Schlussweise

Datenverarbeitungssystem

Rechter Winkel

Familie <Mathematik>

Vorlesung/Konferenz

Bridge <Kommunikationstechnik>

Computeranimation

Zufallsgenerator

19:13

Distributionstheorie

Informationsmodellierung

Klasse <Mathematik>

Mustersprache

Zahlenbereich

Vorlesung/Konferenz

Fréchet-Algebra

Computeranimation

19:35

Folge <Mathematik>

Zwei

Randomisierung

Systemaufruf

Zahlenbereich

Vorlesung/Konferenz

Computeranimation

Zufallsgenerator

20:10

Nichtlinearer Operator

Folge <Mathematik>

Prozess <Physik>

Extrempunkt

Cliquenweite

Zahlenbereich

Element <Mathematik>

Extrempunkt

Kontextbezogenes System

Computeranimation

Zufallsgenerator

Spezielle Funktion

Bildschirmmaske

Weg <Topologie>

Spannweite <Stochastik>

Automatische Indexierung

Stichprobenumfang

Hypermedia

Statistische Analyse

Ablöseblase

Randomisierung

Vorlesung/Konferenz

User Generated Content

22:21

Mailing-Liste

Folge <Mathematik>

Informationsmodellierung

Reelle Zahl

Mailing-Liste

Schlüsselverwaltung

Computeranimation

Informationssystem

23:02

Lineares Funktional

Distributionstheorie

Parametersystem

Multifunktion

Momentenproblem

Familie <Mathematik>

Zahlenbereich

Plot <Graphische Darstellung>

Verteilungsfunktion <Statistische Mechanik>

Ausgleichsrechnung

Speicherbereichsnetzwerk

Computeranimation

Zufallsgenerator

Normalverteilung

Datenfeld

Reelle Zahl

Trennschärfe <Statistik>

Lesen <Datenverarbeitung>

24:12

Metropolitan area network

Distributionstheorie

Lineares Funktional

Punkt

Zellularer Automat

Logarithmus

Schaltnetz

Mathematisches Objekt

Systemaufruf

Computeranimation

Zufallsgenerator

Feuchteleitung

24:46

Distributionstheorie

Zahlenbereich

Dreieck

Computeranimation

Eins

25:11

Soundverarbeitung

Distributionstheorie

Punkt

Winkel

Klasse <Mathematik>

Inzidenzalgebra

Service provider

Computeranimation

Metropolitan area network

Knotenmenge

Normalverteilung

Randomisierung

Ablöseblase

Vorlesung/Konferenz

Delisches Problem

Instantiierung

25:45

Sichtbarkeitsverfahren

Lineares Funktional

Güte der Anpassung

Klasse <Mathematik>

Zahlenbereich

Quellcode

Physikalisches System

Division

Computeranimation

Zufallsgenerator

Arithmetisches Mittel

Informationsmodellierung

Generator <Informatik>

Ungelöstes Problem

Randomisierung

Programmbibliothek

Hill-Differentialgleichung

Fréchet-Algebra

Aggregatzustand

Instantiierung

27:16

Folge <Mathematik>

Zahlenbereich

Geräusch

Ikosaeder

Quellcode

Ein-Ausgabe

Ereignishorizont

Computeranimation

Zufallsgenerator

Portscanner

Fehlertoleranz

Gruppe <Mathematik>

Ein-Ausgabe

Optimierung

Einflussgröße

27:54

Bit

Mathematik

Physikalischer Effekt

Güte der Anpassung

Reihe

Randomisierung

Computeranimation

Zufallsgenerator

28:28

Deskriptive Statistik

Exakter Test

Vorlesung/Konferenz

Generator <Informatik>

Remote Access

Binder <Informatik>

Computeranimation

Zufallsgenerator

### Metadaten

#### Formale Metadaten

Titel | Non Sequitur: An exploration of Python's random module |

Serientitel | EuroPython 2014 |

Teil | 109 |

Anzahl der Teile | 120 |

Autor | Trejo, Jair |

Lizenz |
CC-Namensnennung 3.0 Unported: Sie dürfen das Werk bzw. den Inhalt zu jedem legalen Zweck nutzen, verändern und in unveränderter oder veränderter Form vervielfältigen, verbreiten und öffentlich zugänglich machen, sofern Sie den Namen des Autors/Rechteinhabers in der von ihm festgelegten Weise nennen. |

DOI | 10.5446/19984 |

Herausgeber | EuroPython |

Erscheinungsjahr | 2014 |

Sprache | Englisch |

Produktionsort | Berlin |

#### Inhaltliche Metadaten

Fachgebiet | Informatik |

Abstract | Jair Trejo - Non Sequitur: An exploration of Python's random module An exploration of Python's random module for the curious programmer, this talk will give a little background in statistics and pseudorandom number generation, explain the properties of python's choice of pseudorandom generator and explore through visualizations the different distributions provided by the module. ----- # Audience Non mathematical people who wants a better understanding of Python's random module. # Objectives The audience will understand pseudorandom number generators, the properties of Python's Mersenne Twister and the differences and possible use cases between the distributions provided by the `random` module. # The talk I will start by talking about what randomness means and then about how we try to achieve it in computing through pseudorandom number generators (5 min.) I will give a brief overview of pseudorandom number generation techniques, show how their quality can be assessed and finally talk about Python's Mersenne Twister and why it is a fairly good choice. (10 min.) Finally I will talk about how from randomness we can build generators with interesting probability distributions. I'll compare through visualizations thos provided in Python's `random` module and show examples of when they can be useful in real-life. (10 min.) |

Schlagwörter |
EuroPython Conference EP 2014 EuroPython 2014 |