"기브스 표집"의 두 판 사이의 차이

수학노트
둘러보기로 가기 검색하러 가기
(→‎노트: 새 문단)
 
 
(같은 사용자의 중간 판 하나는 보이지 않습니다)
31번째 줄: 31번째 줄:
 
===소스===
 
===소스===
 
  <references />
 
  <references />
 +
 +
==메타데이터==
 +
===위키데이터===
 +
* ID :  [https://www.wikidata.org/wiki/Q1191905 Q1191905]
 +
===Spacy 패턴 목록===
 +
* [{'LOWER': 'gibbs'}, {'LEMMA': 'sample'}]

2021년 2월 17일 (수) 00:33 기준 최신판

노트

위키데이터

말뭉치

  1. Generalized linear models (i.e. variations of linear regression) can sometimes be handled by Gibbs sampling as well.[1]
  2. Numerous variations of the basic Gibbs sampler exist.[1]
  3. A blocked Gibbs sampler groups two or more variables together and samples from their joint distribution conditioned on all other variables, rather than sampling from each one individually.[1]
  4. A collapsed Gibbs sampler integrates out (marginalizes over) one or more variables when sampling for some other variable.[1]
  5. Like other MCMC methods, the Gibbs sampler constructs a Markov Chain whose values converge towards a target distribution.[2]
  6. Because the Gibbs sampler accepts every draw, it is much more efficient than the MH algorithm.[3]
  7. This algorithm is called a Gibbs sampler.[3]
  8. We have already mentioned that the acceptance rate for a Gibbs sampler is 1 because every draw is accepted.[3]
  9. All posterior estimates are computed directly as sample averages of the Gibbs sampler draws.[4]
  10. Although there are exact ways to do this, we can make use of Gibbs sampling too simulate a Markov chain that will converge to a bivariate Normal.[5]
  11. Because the priors are independent here, we will have to use Gibbs sampling to sample from the posterior distribution of \(\mu\) and \(\tau\).[5]
  12. The Gibbs sampler therefore alternates between sampling from a Normal distribution and a Gamma distribution.[5]
  13. In some Metropolis-Hastings or hybrid Gibbs sampling problems we may have parameters where it is easier to sample from a full conditional of a transformed version of the parameter.[5]
  14. In this paper, the slice-within-Gibbs sampler has been introduced as a method for estimating cognitive diagnosis models (CDMs).[6]
  15. The first study confirmed the viability of the slice-within-Gibbs sampler in estimating CDMs, mainly including G-DINA and DINA models.[6]
  16. In this paper, a sampling method called the slice-within-Gibbs sampler is introduced, in which the identifiability constraints are easy to be imposed.[6]
  17. The slice-within-Gibbs sampler can avoid the boring choices of tunning parameters in the MH algorithm and converges faster over the MH algorithm with misspecified proposal distributions.[6]
  18. Now express the problem of learning the parameters of the above network as a Bayesian network and then use Gibbs sampling to estimate the parameters.[7]
  19. This tutorial looks at one of the work horses of Bayesian estimation, the Gibbs sampler.[8]
  20. Use the Gibbs sampler to generate bivariate normal draws.[8]
  21. The Gibbs sampler draws iteratively from posterior conditional distributions rather than drawing directly from the joint posterior distribution.[8]
  22. The Gibbs sampler works by restructuring the joint estimation problem as a series of smaller, easier estimation problems.[8]
  23. It does so by executing Gibbs sampling steps on an extended target distribution defined on the space of the auxiliary variables generated by an interacting particle system.[9]
  24. To illustrate this Gibbs sampling algorithm, we analyse site‐occupancy data of the blue hawker, Aeshna cyanea (Odonata, Aeshnidae), a common dragonfly species in Switzerland.[10]
  25. It then reports an algorithm employing Gibbs sampling to find local sequence regularities and applies that algorithm to demonstrate the subsequence regularities present in sociological articles.[11]

소스

메타데이터

위키데이터

Spacy 패턴 목록

  • [{'LOWER': 'gibbs'}, {'LEMMA': 'sample'}]