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Non Sequitur: An exploration of Python's random module
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Erkannte Entitäten
Sprachtranskript
00:16
please welcome J is gonna tell that random module thank you nonsequitur 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 realworld 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
04:48
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
06:23
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
07:26
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 pseudorandom 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
11:41
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
16:37
large linear fit useful dimension a way that permits as it
16:42
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
18:44
when it directly usable with more and and besides the regular source of further assume a system like at the keyboard or the
18:51
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
19:15
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
19:36
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 usergenerated 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
21:59
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
25:46
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
27:48
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
28:58
interim
00:00
Folge <Mathematik>
Arithmetischer Ausdruck
Zufallszahlen
Randomisierung
Vorlesung/Konferenz
Lateinisches Quadrat
Modul
QuickSort
Modul
00:46
Zufallszahlen
Randomisierung
Computerunterstütztes Verfahren
Operations Research
Softwareentwickler
Computeranimation
Modul
01:18
Arithmetisches Mittel
Antwortfunktion
Vorhersagbarkeit
Vorlesung/Konferenz
Wort <Informatik>
01:43
Videospiel
Nichtlinearer Operator
Newton, Isaac
Prozess <Physik>
Mathematik
Reihe
Term
Physikalische Theorie
Computeranimation
Arithmetisches Mittel
Kalkül
Wurzel <Mathematik>
Analysis
02:33
Reihe
Zahlenbereich
Vorlesung/Konferenz
Information
LieGruppe
Computeranimation
02:57
Folge <Mathematik>
Prozess <Physik>
Randomisierung
Zahlenbereich
Kartesische Koordinaten
Computeranimation
Zufallsgenerator
Lesen <Datenverarbeitung>
03:24
Statistik
Folge <Mathematik>
Punkt
Gewichtete Summe
Kategorie <Mathematik>
Relativitätstheorie
Ereignishorizont
Computeranimation
Zufallsgenerator
Diskrete Simulation
Stichprobenumfang
Wort <Informatik>
Instantiierung
04:00
Folge <Mathematik>
Spannweite <Stochastik>
Digitalsignal
Umkehrung <Mathematik>
Mittelwert
Diskrete Simulation
Zahlzeichen
Uniforme Struktur
Zellularer Automat
Zahlenbereich
Quellcode
Frequenz
Computeranimation
04:46
Telekommunikation
Anonymisierung
Web Site
Computersicherheit
Nummerung
Elektronische Unterschrift
Zufallsgenerator
Algorithmus
Gruppe <Mathematik>
Randomisierung
Schlüsselverwaltung
MessagePassing
Aggregatzustand
Fehlermeldung
05:37
Folge <Mathematik>
Computersicherheit
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Zahlenbereich
Vorlesung/Konferenz
Kartesische Koordinaten
Biprodukt
Zufallsgenerator
06:05
Folge <Mathematik>
Zahlenbereich
Default
Computeranimation
Zufallsgenerator
06:41
Folge <Mathematik>
Ellipse
Vorhersagbarkeit
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Konfiguration <Informatik>
Zufallsgenerator
07:11
Informationsmodellierung
Ortsoperator
MiniDisc
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07:40
Soundverarbeitung
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Computer
Computerunterstütztes Verfahren
QuickSort
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Tabelle <Informatik>
Zufallsgenerator
08:42
Topologische Algebra
Zahlenbereich
Mathematik
EinAusgabe
Computeranimation
Zufallsgenerator
Streaming <Kommunikationstechnik>
Zufallszahlen
Vorlesung/Konferenz
Simulation
Information
HillDifferentialgleichung
Pseudozufallszahlen
Aggregatzustand
09:26
Eindringerkennung
Lokales Netz
Prinzip der gleichmäßigen Beschränktheit
Folge <Mathematik>
Bildschirmmaske
Uniforme Struktur
Randomisierung
Applet
Computeranimation
Verdünnung <Bildverarbeitung>
Instantiierung
Zufallsgenerator
10:08
Folge <Mathematik>
Topologische Algebra
Einheit <Mathematik>
Kategorie <Mathematik>
Mittelwert
Dynamisches RAM
Randomisierung
Zahlenbereich
Computeranimation
10:34
Prognoseverfahren
Prozess <Physik>
Validität
Randomisierung
Zahlenbereich
Vorhersagbarkeit
Einflussgröße
Bildgebendes Verfahren
RaumZeit
Computeranimation
Instantiierung
11:11
Primzahl
Gruppenoperation
Familie <Mathematik>
Passwort
RaumZeit
Computeranimation
11:34
Prognoseverfahren
Ortsoperator
Kategorie <Mathematik>
Allgemeine Relativitätstheorie
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Mustersprache
Randomisierung
Versionsverwaltung
Zahlenbereich
Vorlesung/Konferenz
Computeranimation
12:01
Softwaretest
Distributionstheorie
Subtraktion
Bit
Folge <Mathematik>
Quadratzahl
Thumbnail
Rangstatistik
Mittelwert
Randomisierung
Schlussregel
Abstand
Element <Mathematik>
Computeranimation
12:53
Softwaretest
Folge <Mathematik>
Dicke
LikelihoodFunktion
Zahlenbereich
Kartesische Koordinaten
Frequenz
Komplex <Algebra>
QuickSort
Computeranimation
Zufallszahlen
Uniforme Struktur
Mustersprache
Randomisierung
Vorlesung/Konferenz
Einflussgröße
Instantiierung
14:11
Softwaretest
Folge <Mathematik>
Mathematik
Reihe
Zahlenbereich
MailingListe
Computeranimation
Zufallsgenerator
Umkehrung <Mathematik>
Zufallszahlen
Zahlenbereich
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Optimierung
FréchetAlgebra
14:47
Folge <Mathematik>
Rechter Winkel
Relativitätstheorie
Randomisierung
Vorlesung/Konferenz
Normalspannung
FréchetAlgebra
Dialekt
Computeranimation
Zufallsgenerator
15:23
Folge <Mathematik>
Topologische Algebra
Extrempunkt
Oktave <Mathematik>
HausdorffDimension
Zahlenbereich
Gleichungssystem
Anfangswertproblem
pBlock
Frequenz
Computeranimation
Differenzengleichung
Menge
Randomisierung
Gerade
Standardabweichung
16:35
Folge <Mathematik>
Lineare Regression
HausdorffDimension
Frequenz
Computeranimation
Zufallsgenerator
Aggregatzustand
17:01
Folge <Mathematik>
Menge
Kategorie <Mathematik>
HausdorffDimension
Stichprobenumfang
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Zahlenbereich
Vorlesung/Konferenz
Korrelationsfunktion
Zufallsgenerator
17:35
Topologische Algebra
Baum <Mathematik>
Formale Sprache
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Quellcode
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Zufallsgenerator
Quellcode
Dienst <Informatik>
Zufallszahlen
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Mereologie
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Entropie
Default
Modul
18:42
Schnelltaste
Bildschirmmaske
Stabilitätstheorie <Logik>
Hardware
Statistische Schlussweise
Datenverarbeitungssystem
Rechter Winkel
Familie <Mathematik>
Vorlesung/Konferenz
Bridge <Kommunikationstechnik>
Zufallsgenerator
19:13
Distributionstheorie
Informationsmodellierung
Zufallszahlen
Topologische Algebra
Klasse <Mathematik>
Mustersprache
Zahlenbereich
Vorlesung/Konferenz
Computeranimation
19:35
Folge <Mathematik>
Zufallszahlen
Zwei
Randomisierung
Systemaufruf
Zahlenbereich
Vorlesung/Konferenz
Computeranimation
Zufallsgenerator
20:10
Nichtlinearer Operator
Folge <Mathematik>
Prozess <Physik>
Extrempunkt
Stichprobe
Cliquenweite
Zahlenbereich
Element <Mathematik>
Kontextbezogenes System
Computeranimation
Zufallsgenerator
Spezielle Funktion
Auswahlaxiom
Bildschirmmaske
Weg <Topologie>
Spannweite <Stochastik>
Zufallszahlen
Automatische Indexierung
Hypermedia
Stichprobenumfang
Randomisierung
Ablöseblase
User Generated Content
22:21
Folge <Mathematik>
Informationsmodellierung
Zufallszahlen
Reelle Zahl
MailingListe
Schlüsselverwaltung
Computeranimation
23:02
Distributionstheorie
Parametersystem
Lineares Funktional
Multifunktion
Momentenproblem
Familie <Mathematik>
Zahlenbereich
Plot <Graphische Darstellung>
Verteilungsfunktion <Statistische Mechanik>
Computeranimation
Zufallsgenerator
Datenfeld
Normalverteilung
Reelle Zahl
Trennschärfe <Statistik>
Lesen <Datenverarbeitung>
24:12
Distributionstheorie
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Punkt
Zellularer Automat
Schaltnetz
Mathematisches Objekt
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24:46
Distributionstheorie
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Dreieck
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Gammafunktion
Eins
25:11
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Distributionstheorie
Punkt
Kreisfläche
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Klasse <Mathematik>
Inzidenzalgebra
Service provider
Computeranimation
Knotenmenge
Normalverteilung
Einheit <Mathematik>
Randomisierung
Ablöseblase
Vorlesung/Konferenz
Delisches Problem
Instantiierung
25:45
Sichtbarkeitsverfahren
Lineares Funktional
Topologische Algebra
Klasse <Mathematik>
Güte der Anpassung
Zahlenbereich
Physikalisches System
Quellcode
Division
Computeranimation
Zufallsgenerator
Arithmetisches Mittel
Physikalisches System
Informationsmodellierung
Zufallszahlen
Ungelöstes Problem
Programmbibliothek
Randomisierung
FréchetAlgebra
Instantiierung
Aggregatzustand
27:16
Folge <Mathematik>
Mathematisierung
Geräusch
Zahlenbereich
Quellcode
EinAusgabe
Ereignishorizont
Computeranimation
Zufallsgenerator
Fehlertoleranz
Quellcode
Zufallszahlen
Zahlenbereich
Gruppe <Mathematik>
EinAusgabe
Entropie
Messprozess
Optimierung
Einflussgröße
27:54
Bit
Mathematik
Physikalischer Effekt
Güte der Anpassung
Reihe
Randomisierung
Computer
Optimierung
Computeranimation
Spezifisches Volumen
Zufallsgenerator
28:28
Kryptologie
Binder <Informatik>
NonstandardAnalysis
Zufallsgenerator
Computeranimation
Deskriptive Statistik
Topologische Algebra
Zufallszahlen
Softwaretest
Exakter Test
Vorlesung/Konferenz
Hintertür <Informatik>
Generator <Informatik>
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 
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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 reallife. (10 min.) 
Schlagwörter 
EuroPython Conference EP 2014 EuroPython 2014 