Closed LuckyTiger closed 6 years ago
How to get predicted latent variance?
I think it is all well described:
https://github.com/SheffieldML/GPy/blob/devel/GPy/inference/latent_function_inference/inferenceX.py
On 18 May 2018, at 09:18, LuckyTiger notifications@github.com wrote:
How to get predicted latent variance?
— You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub, or mute the thread.
But this don't have X's variance
If the model you begin with is a Bayesian GPLVM the output of inferX will be a variational posterior, which has the means and variances attached.
But how did Lawrence's first GP-LVM paper https://papers.nips.cc/paper/2540-gaussian-process-latent-variable-models-for-visualisation-of-high-dimensional-data.pdf have uncertainty plotted?
Ah, try looking into the plotting function plot_latent for details on that measure.
On 19 May 2018, at 07:49, LuckyTiger notifications@github.com wrote:
But how did Lawrence's first GP-LVM paper https://papers.nips.cc/paper/2540-gaussian-process-latent-variable-models-for-visualisation-of-high-dimensional-data.pdf have uncertainty plotted?
— You are receiving this because you modified the open/close state. Reply to this email directly, view it on GitHub, or mute the thread.
I find https://github.com/SheffieldML/GPy/issues/395 very helpful. using
m_gplvm.infer_newX(Y_new)
is ok. But how does it work?