this is for holding javascript data
Vadim Kosoy edited The_following_theorem_is_the__.tex
about 8 years ago
Commit id: 298a12dfd7c364c8bf96ceacc6a4f8a6a7ea45d0
deletions | additions
diff --git a/The_following_theorem_is_the__.tex b/The_following_theorem_is_the__.tex
index 25d72de..3d24b67 100644
--- a/The_following_theorem_is_the__.tex
+++ b/The_following_theorem_is_the__.tex
...
$$\E_{\mu^k \times U^{\R_P(k,j)}}[(P^{kj} - f)^2] \leq \E_{\mu^k \times U^{\R_S(k,j)}}[(P^{kj} + t(k,j)S^{kj} + \varepsilon_t^{kj} - f)^2] + \varepsilon(k,j)$$
$$\E_{\mu^k \times U^{\R_P(k,j)}}[(P^{kj} $$\E[(P^{kj} - f)^2] \leq
\E_{\mu^k \times U^{\R_S(k,j)}}[(P^{kj} \E[(P^{kj} + t(k,j)S^{kj} - f)^2] + 2
\E_{\mu^k \times U^{\R_S(k,j)}}[(P^{kj} \E[(P^{kj} + t(k,j)S^{kj} - f) \varepsilon_t^{kj}] +
\E_{\mu^k \times U^{\R_S(k,j)}}[(\varepsilon_t^{kj})^2] \E[(\varepsilon_t^{kj})^2] + \varepsilon(k,j)$$
BLAH