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Data Fields | |
| double * | xinit |
| double | xl |
| double | xr |
| double | convex |
| int | ninit |
| int | npoint |
| char | do_metro |
| double | xprev |
| int | neval |
| arms_state * | state |
| apop_model * | model |
to perform derivative-free adaptive rejection sampling with metropolis step
| double apop_arms_settings::convex |
Adjustment for convexity
| char apop_arms_settings::do_metro |
Whether metropolis step is required. (I.e., set to one if you're not sure if the function is log-concave). Set to 'y'es or 'n'o
| apop_model* apop_arms_settings::model |
The model from which I will draw. Mandatory. Must have either a log_likelihood or p method.
| int apop_arms_settings::neval |
On exit, the number of function evaluations performed
| int apop_arms_settings::ninit |
Number of starting values supplied (i.e. number of elements in xinit)
| int apop_arms_settings::npoint |
Maximum number of envelope points. I malloc space for this many doubles at the outset. Default = 1e5.
| double* apop_arms_settings::xinit |
A double* giving starting values for x in ascending order. Default: -1, 0, 1. If this isn't NULL, I need at least three items.
| double apop_arms_settings::xl |
Left bound. If you don't give me one, I'll use min[min(xinit)/10, min(xinit)*10].
| double apop_arms_settings::xprev |
Previous value from Markov chain
| double apop_arms_settings::xr |
Right bound. If you don't give me one, I'll use max[max(xinit)/10, max(xinit)*10].