### Merge pull request #227 from resibots/improve_doc [ci skip]

Fixes in docs
parents 364bcc58 5fd050e2
 ... ... @@ -55,7 +55,7 @@ A GP is fully specified by its mean function :math:\mu(\mathbf{x}) and covaria .. math:: k_{SE}(\chi_1, \chi_2) = \sigma_f^2 \cdot \exp\left( \frac{\left|\left|\chi_1, \chi_2\right|\right|^2}{2 l^2} \right) k_{SE}(\chi_1, \chi_2) = \sigma_f^2 \cdot \exp\left( -\frac{\left|\left|\chi_1 - \chi_2\right|\right|^2}{2 l^2} \right) For some datasets, it makes sense to hand-tune these parameters (e.g., when there are very few samples). Ideally, our objective should be to learn :math:l^2 (characteristic length scale) and :math:\sigma_f^2 (overall variance). ... ...
 ... ... @@ -63,7 +63,7 @@ namespace limbo { Exponential kernel (see :cite:brochu2010tutorial p. 9). .. math:: k(v_1, v_2) = \sigma^2\exp \Big(-\frac{1}{l^2} ||v_1 - v_2||^2\Big) k(v_1, v_2) = \sigma^2\exp \Big(-\frac{||v_1 - v_2||^2}{2l^2}\Big) Parameters: - double sigma_sq (signal variance) ... ...
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