sim_Friedman1

The regression problem Friedman 1 as described in Friedman (1991) and
Breiman (1996). Inputs are 10 independent variables uniformly
distributed on the interval $[0,1]$, only 5 out of these 10 are actually
used. Outputs are created according to
the formula
$$y = 10 \sin(\pi x1 x2) + 20 (x3 - 0.5)^2 + 10 x4 + 5 x5 + e$$
 where e is $N(0,sd^2)$.

Bayesian Additive Regression Kernels (BARK) provides
an implementation for non-parametric function estimation using Levy
Random Field priors for functions that may be represented as a
sum of additive multivariate kernels. Kernels are located at
every data point as in Support Vector Machines, however, coefficients
may be heavily shrunk to zero under the Cauchy process prior, or even,
set to zero. The number of active features is controlled by priors on
precision parameters within the kernels, permitting feature selection. For
more details see Ouyang, Z (2008) "Bayesian Additive Regression Kernels",
Duke University. PhD dissertation, Chapter 3 and Wolpert, R. L, Clyde, M.A,
and Tu, C. (2011) "Stochastic Expansions with Continuous Dictionaries Levy
Adaptive Regression Kernels, Annals of Statistics Vol (39) pages 1916-1962
<doi:10.1214/11-AOS889>.

Merlise Clyde

bark

Bayesian Additive Regression Kernels

Zhi Ouyang

Robert Wolpert

sim_Friedman1 function

<dl><dt>n</dt>
<dd>number of data points to create</dd>
<dt>sd</dt>
<dd>standard deviation of noise, with default value 1</dd></dl>

Arguments

Simulated Regression Problem Friedman 1 — sim_Friedman1

<dl>

<dt>n</dt>
<dd>number of data points to create</dd>


<dt>sd</dt>
<dd>standard deviation of noise, with default value 1</dd>

</dl>

sim_Friedman1: Simulated Regression Problem Friedman 1

Description

Usage

Value

Arguments

References

See Also

Examples