100% tevredenheidsgarantie Direct beschikbaar na je betaling Lees online óf als PDF Geen vaste maandelijkse kosten
logo-home
Eerder door jou gezocht
Eerder door jou gezocht
logo
Bayesian Statistical Methods 1st Edition By Brian J. Reich; Sujit K. Ghosh 9781032093185 ALL Chapters . €17,17
In winkelwagen
Snel navigeren naar
Voorbeeld
  2.  
  3. Bayesian Statistical Methods 1st Edition By Brian
  4.  
  5. Bayesian Statistical Methods 1st Edition By Brian

Tentamen (uitwerkingen)

Bayesian Statistical Methods 1st Edition By Brian J. Reich; Sujit K. Ghosh 9781032093185 ALL Chapters .
 0 keer verkocht
  • Vak
  • Bayesian Statistical Methods 1st Edition By Brian
  • Instelling
  • Bayesian Statistical Methods 1st Edition By Brian
  • Boek
  • Bayesian Statistical Methods

Bayesian Statistical Methods 1st Edition By Brian J. Reich; Sujit K. Ghosh 9781032093185 ALL Chapters .

Voorbeeld 1 van de 2  pagina's

Document informatie

  • 22 augustus 2024
  • 2
  • 2024/2025
  • Tentamen (uitwerkingen)
  • Vragen en antwoorden

Onderwerpen

  • bayesian statistical methods 1st edition by brian
  • bayesian statistical methods 1st edition reich
  • bayesian statistical methods 1st edition ghosh
  • bayesian statistical methods

Gekoppeld boek

book image

Titel boek:

Auteur(s):

  • Uitgave:
  • ISBN:
  • Druk:

Geschreven voor

  • Bayesian Statistical Methods 1st Edition By Brian
Alle documenten voor dit vak (3)

Verkoper

avatar-seller
phinta004

Ontvangen beoordelingen

Voorbeeld van de inhoud

Solutions Manual For Bayesian Statistical Methods 1st Edition
By Brian J. Reich; Sujit K. Ghosh 9781032093185 ALL Chapters .
in general, the goal of statistics is to - ANSWER: make an inference using observed data,x
to make an inference, the data is assumed to have originated from a probability model _________
and our inference is usually expressed in terms of an unobservable parameter ____ of the distribution
___ - ANSWER: f(x|θ) θ f
The Frequentist approach to statistics adopts the view that - ANSWER: only the data, x, is random
The fundamental principle of Bayesian statistics: everything is _______________ and can be treated
as ______________ and be associated with its own _________________________ provided we use a
_________________ interpretation of probability - ANSWER: uncertain, random, probability
distribution, subjective
in the Bayesian approach, the parameter θ is treated as a - ANSWER: random variable
the fundamental procedure of Bayesian statistics: use _______________________ probability with
f(θ) and f(x|θ) to find f(θ|x), which is the __________________ pdf of the parameter θ given the data
x - ANSWER: conditional
In the Bayesian approach, large samples aren't necessary for the methods to be valid but in some
cases with ________________________________ the results agree with the standard Frequentist
methods - ANSWER: large amounts of data
In the Frequentist approach, the parameter θ is considered as a - ANSWER: fixed but unknown value
In the Frequentist approach the sample size n has to be ____________ but in the Bayesian approach
it can be ______________________ - ANSWER: large, any size
The frequentist approach is based on ____________ whereas the Bayesian approach is based on
___________________________ - ANSWER: likelihood, likelihood combined with prior probabilities
What still apply to both methods? - ANSWER: the laws of probability
Bayesian: We use probability to describe all - ANSWER: uncertainty
Let X and Y be two continuous random variables with joint pdf f(x,y), then the marginal pdf of Y is
[NOT IN TERMS OF THE MULTIPLICATION RULE] - ANSWER: f(y) = ∫(X)f(x,y)dx (NOTE: (X) lies at the
bottom of the integral it is not included in the integral - see notes)
Let X and Y be two continuous random variables with joint pdf f(x,y), then the conditional pdf of Y
given X=x is - ANSWER: f(y|x) = f(x,y)/f(x)
f(x,y) = ..... = ...... = ....... [multiplication rule] - ANSWER: f(x|y)f(y) = f(y|x)f(x) = f(y,x)
f(x1,......,xn) = .........[generalised result of multiplication rule] - ANSWER: f(x1|x2,..,xn)f(x2|
x3,...,xn)...f(x(n-1)|xn)f(xn)
Let X and Y be two continuous random variables with joint pdf f(x,y), then the marginal pdf of Y is [IN
TERMS OF THE MULTIPLICATION RULE] - ANSWER: f(y) = ∫(X)f(y|x)f(x)dx (NOTE: (X) is the sample
space lies at the bottom of the integral it is not included in the integral - see notes) this is the
continuous version of the Partition Theorem
Let X and Y be two continuous random variables with joint pdf f(x,y), then the marginal pdf of Y
conditioned on a further variable Z is [IN TERMS OF THE MULTIPLICATION RULE] - ANSWER: f(y|z)=
∫(X)f(y|x,z)f(x|z)dx (NOTE: (X) is the sample space lies at the bottom of the integral it is not included in
the integral - see notes)
X and Y are independent if and only if f(x,y) = - ANSWER: f(x)f(y), so f(x)=f(x|y) and vv
Suppose X, Y and Z are random variables with f(y,z)>0, then X and Y are conditionally independent
given Z if and only if - ANSWER: f(x,y|z)=f(x|z)f(y|z), so f(x|y,z)=f(x|z) and vv
Suppose X and Y are continuous random variables and f(x)>0, then applying the partition theorem to
the denominator gives f(y|x) = - ANSWER: [f(x|y)f(y)]/[f(x)] = [f(x|y)f(y)]/[∫f(x|y)f(y)dy]
the prior distribution is represented by - ANSWER: f(θ)
After observing the data, X = x, you can formulate the likelihood, represented by_______ , of seeing
the data given the model or parameter value θ - ANSWER: f(x|θ)
the posterior distribution is represented by ____________ and expresses our uncertainty about the
value of the parameter value or statistical model θ given the information we have learned after we
have seen the data - ANSWER: f(θ|x)
Bayes theorem f(θ|x)= - ANSWER: (f(x|θ)f(θ))/f(x)
posterior = - ANSWER: (likelihood x prior)/data probability
f(θ|x)∝ - ANSWER: f(x|θ)f(θ)

Dit zijn jouw voordelen als je samenvattingen koopt bij Stuvia:

Bewezen kwaliteit door reviews

Bewezen kwaliteit door reviews

Studenten hebben al meer dan 850.000 samenvattingen beoordeeld. Zo weet jij zeker dat je de beste keuze maakt!

In een paar klikken geregeld

In een paar klikken geregeld

Geen gedoe — betaal gewoon eenmalig met iDeal, creditcard of je Stuvia-tegoed en je bent klaar. Geen abonnement nodig.

Direct to-the-point

Direct to-the-point

Studenten maken samenvattingen voor studenten. Dat betekent: actuele inhoud waar jij écht wat aan hebt. Geen overbodige details!

Veelgestelde vragen

Wat krijg ik als ik dit document koop?

Je krijgt een PDF, die direct beschikbaar is na je aankoop. Het gekochte document is altijd, overal en oneindig toegankelijk via je profiel.

Tevredenheidsgarantie: hoe werkt dat?

Onze tevredenheidsgarantie zorgt ervoor dat je altijd een studiedocument vindt dat goed bij je past. Je vult een formulier in en onze klantenservice regelt de rest.

Van wie koop ik deze samenvatting?

Stuvia is een marktplaats, je koop dit document dus niet van ons, maar van verkoper phinta004. Stuvia faciliteert de betaling aan de verkoper.

Zit ik meteen vast aan een abonnement?

Nee, je koopt alleen deze samenvatting voor €17,17. Je zit daarna nergens aan vast.

Is Stuvia te vertrouwen?

4,6 sterren op Google & Trustpilot (+1000 reviews)

Afgelopen 30 dagen zijn er 66184 samenvattingen verkocht

Opgericht in 2010, al 15 jaar dé plek om samenvattingen te kopen

Begin nu gratis
€17,17
  • (0)
In winkelwagen
Toegevoegd