在R和C ++中的Gibbs采样 [英] Gibbs Sampling in R and C++

问题描述

我已经检查过不同编程语言的吉布斯抽样; in R

I have checked gibbs sampling in different programming languages; in R

  x <- rgamma(1,3,y*y+4)
  y <- rnorm(1,1/(x+1),1/sqrt(2*(x+1)))

in c ++

  x = R::rgamma(3.0,1.0/(y*y+4));
  y = R::rnorm(1.0/(x+1),1.0/sqrt(2*x+2));

如果它使用R函数为什么它在c ++中不同，因为rgamma不需要n =观察次数，采用scale而不是rate作为默认输入，rnorm没有n =观察数。

If it uses R functions why it differs in c++ as rgamma takes no n=number of observation and it takes scale instead of rate as default input and rnorm has no n= number of observation as well.

对于Rcpp，它是完全不同的，如：

For Rcpp it is totaly different such as;

 y = ::Rf_rnorm(1.0/(x+1),1.0/sqrt(2*x+2));

推荐答案

R :: rgamma（）也来自Rcpp。它方便地包装C级非命名空间 :: Rf_rnorm（）。

R::rgamma() is from Rcpp too. It conveniently wraps the C-level, non-namespaced ::Rf_rnorm().

注意， Rcpp :: rnorm（） - 并且有很多Gibbs采样器示例，在Darren Wilkinson的初始发布之后。我们最好的例子可能是 Rcpp画廊的此页。

Note that you also have the vectorised Rcpp::rnorm() -- and that there are plenty of Gibbs Sampler examples out there following the initial post by Darren Wilkinson. Our best example may be this page at the Rcpp Gallery.

编辑：由于您对 shape = 1 / rate 参数化显然感到困惑，并为您工作的示例：

And as you are evidently confused over the shape = 1/rate parameterization, here is a complete and worked example for you:

我们编译一个方便的R函数通过Rcpp调用C ++：

We compile a convenience R function calling C++ via Rcpp first:

R> cppFunction("NumericVector callrgamma(int n, double shape, double scale) { 
+                  return(rgamma(n, shape, scale)); }")
R>

然后我们调用R，确保修复种子：

We then call R, making sure we fix the seed:

R> set.seed(42); rgamma(3, 2.0, 2.0)   # calling R
[1] 1.824478 0.444055 0.779610
R>

现在，使用相同的种子，我们调用C ++函数我们确保我们尊重1 / over重新参数化：

Now, using the same seed, we call the C++ function and we make sure we respect the "1/over" reparameterization as well:

R> set.seed(42); callrgamma(3, 2.0, 1/2.0)   # calling Rcpp
[1] 1.824478 0.444055 0.779610
R> 

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