Difference between revisions of "Documentation:Monte Carlo Equilibration"
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= \frac{\sum_i (x_i - \bar{x}_i)^2 \mathrm{Var}(y_i) }{(\sum_i (x_i - \bar{x}_i)^2)^2} | = \frac{\sum_i (x_i - \bar{x}_i)^2 \mathrm{Var}(y_i) }{(\sum_i (x_i - \bar{x}_i)^2)^2} | ||
</math> | </math> | ||
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+ | Denoting <math>\mathrm{Var}(y_i) = \sigma_{y}^2 </math>, we have: | ||
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+ | <math> \mathrm{Var}({\beta_1}) = \frac{\sigma_y^2}{\sum_i (x_i - \bar{x}_i)^2} </math> |
Revision as of 12:22, 9 September 2013
Contents
Monte Carlo equilibration
Theory
We have a timeseries of N measurements obtained from a Monte Carlo simulation, i.e. .
Suppose (s.t.
) is the least-squares best fitted line, we attempt to minimize
w.r.t.
and
.
,
:
Slope of best-fitted line
Error in slope of best-fitted line
Denoting , we have: