## FANDOM

32.675 Seiten

 Typus KomplettPlagiat Bearbeiter Graf Isolan Gesichtet
Untersuchte Arbeit:
Seite: 15, Zeilen: 5-16
Quelle: Sun 2007
Seite(n): 14, Zeilen: 7-17
The fundamental idea of Bayesian inference is that both the model parameters (θ) and the observed data are considered as random variables and are modeled using probability distributions (Gelman et al., 1995). The parameters are given a prior distribution, P(θ), then through the likelihood function, P(Y/θ), the parameter can be estimated from the posterior density, P(θ/Y) ∝ P(θ)P(Y/θ). All of the above four methods therefore treat the unknown haplotypes of each individual as random variables. The main dierence [sic] between using the EM algorithm and a Bayesian method to do haplotype inference is whether the haplotype frequencies in the population are treated as random variables or not. Another important common aspect of these above four methods is that they all used Markov Chain Monte Carlo (MCMC) methods to sample from the posterior distribution.

Gelman, A., Carlin, J. B., Stern, H. S. & Rubin, D. B. (1995) Bayesian Data Analysis. Chapman and Hall, Canberra.

The fundamental idea of Bayesian inference is that both the model parameters (θ) and the observed data are considered as random variables and are modeled using probability distributions (Gelman et al. 1995). The parameters are given a prior distribution, P(θ), then through the likelihood function, P(Y|θ), the parameter can be estimated from the posterior density, P(θ|Y) ∝ P(θ)P(Y|θ). All of the above four methods therefore treat the unknown haplotypes of each individual as random variables. The main difference between using the EM algorithm and a Bayesian method to do haplotype inference is whether the haplotype frequencies in the population are treated as random variables or not. Another important common aspect of these above four methods is that they all used Markov Chain Monte Carlo (MCMC) methods to sample from the posterior distribution.

Gelman, A., Carlin, J. B., Stern, H. S. and Rubin, D. B. (1995). Bayesian Data Analysis, 1st edn, Chapman & Hall, Canberra.

 Anmerkungen Though directly copied, nothing has been marked as a citation. The missing "ff" in "difference" stems from the faulty copying-process as there is an uncopyable special character in the original text. Sichter (Graf Isolan)