Regression techniques similar to Kriging/Gaussian process regression I am looking for regression techniques which are similar to Kriging/Gaussian process regression, in that no explicit model needs to be specified. (Discounting the prior over functions) I have three independent variables and one dependent variable to which I want to apply such a procedure. The independent variables specify coordinates (locations in 3D), while the dependent variable specifies Wi-Fi signal strength at the given coordinates. Since it is hard to appropriately visualize such high dimensional data, techniques without explicit model dependence are of primary interest. The only similar technique I found was the somewhat unsophisticated Nearest-neighbour interpolation.
Are the above-mentioned techniques the only choices for such a problem?
 A: I would add a more detailed description of the problem you are trying to solve as your question is very vague. What are you trying to attack with Kriging/Gaussian process regression? Any clue on the nature of your problem could really help you get a better answer. Anyway, you could basically use some of a wide array of non-parametric machine learning algorithms. For example: CART, random forest, boosted regression trees, etc. Even though you don't want to specify an explicit model, you do have a problem of the form: $Y:=X_{1}+X_{2}+X_{3}$. You can fit any of these models to your data, for example, fitting random forest in R would go something like this: 
fittedmodel <- randomForest(Y~X1+X2+X3,data=yourdata, ntree=1000)

fittedmodel <- randomForest(Y=yourdata[,1],X=yourdata[,2:4], ntree=1000)

The random forest R implementation has more parameters to play with, but in general is a model that requires little tuning so just using the defaults as I am doing here can give acceptable results.
What are you doing this for? if you want to interpolate a map, you can then use this trained model on your unlabelled observations, that simply must have values for the independent variables ($X_{1}, X_{2}, X_{3}$):
interpolation <- predict(fittedmodel,unlabeleddata)

Also, if you are interpolating a map, introducing the coordinates as independent variables is sometimes quite helpful.
A: You could try Locally Weighted Linear Regression (wiki article). In fact there is a connection between the two techniques (local regression and GP regression) as described in pg. 26 of the GPML book.
