add Gaussian spatial covariance #520
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danielinteractive
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Thanks a lot @clarkliming for starting this!
Just let me know when you are ready for review
| // partial sigma / partial theta_1 = sigma. | ||
| auto sigma = this->get_sigma(visits, dist); | ||
| ret.block(0, 0, dist.rows(), dist.cols()) = sigma; | ||
| ret.block(dist.rows(), 0, dist.rows(), dist.cols()) = sigma.array() * dist.array().suqare() * (1 - this->rho); |
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| ret.block(dist.rows(), 0, dist.rows(), dist.cols()) = sigma.array() * dist.array().suqare() * (1 - this->rho); | |
| ret.block(dist.rows(), 0, dist.rows(), dist.cols()) = sigma.array() * dist.array().square() * (1 - this->rho); |
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this is still there
| auto dtheta1dtheta2 = sigma.array() * dist.array().suqare() * rho_r; | ||
| ret.block(dist.rows(), 0, dist.rows(), dist.cols()) = dtheta1dtheta2; | ||
| ret.block(dist.rows() * 2, 0, dist.rows(), dist.cols()) = dtheta1dtheta2; | ||
| matrix<Type> dtheta2s = dtheta1dtheta2 * (dist.array().suqare() * rho_r - this->rho); |
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here the typo is again
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close #501
this is a draft and a lot of files need to be updated; given low capacity at the moment I will need to do it slowly.
The core part is simple and has been updated; the documents, tests etc are to be completed