The following code is exactly copied from the [documentation][1] on Predict using Gaussian Process. 

    data = {-1.2 -> 1.2, 1.4 -> 1.4, 3.1 -> 1.8, 4.5 -> 1.6}; 
    p = Predict[data, Method -> "GaussianProcess"]

    Show[Plot[{
         p[x],
         p[x] + StandardDeviation[p[x, "Distribution"]], 
         p[x] - StandardDeviation[p[x, "Distribution"]]
        }, {x, -2, 6}, PlotStyle -> {Blue, Gray, Gray}, 
        Filling -> {2 -> {3}}, Exclusions -> False, 
        PerformanceGoal -> "Speed", 
        PlotLegends -> {"Prediction", "Confidence Interval"}], 
        ListPlot[List @@@ data, PlotStyle -> Red, PlotLegends -> {"Data"}]]


It produces the following figure:
[![enter image description here][2]][2]

It's really weird that the point {1.4, 1.4} is far from the predictive mean, represented by the blue line. Interestingly, in introducing the new features on Version 11., the official site gives exactly the [same example][3], but with a different, and more 'reasonable', result, shown in the following figure:
 [![enter image description here][4]][4]


So I am wondering if someone could help explain what is happening here. Since Bayesian optimization also uses Gaussian Process as far as I know, I am also worrying about the Bayesian optimization implementation too...

  [1]: http://reference.wolfram.com/language/ref/method/GaussianProcess.html
  [2]: https://i.sstatic.net/LxY4B.png
  [3]: https://www.wolfram.com/language/11/improved-machine-learning/visualize-the-predictions-of-a-gaussian-process-mo.html?product=language
  [4]: https://i.sstatic.net/ZMyp4.png