As with a lot of optimization, it depends on where you start. The simple model I gave before gives a pretty good example.

```python
with Model() as model:
    p = Beta('p',5,5)
    y = Binomial('y',n=20,p=p,observed=15)
    y_sim = Binomial('y',n=20,p=p)
```
The MAP for this should be `y_sim=14` and approximately `p=.7`. `find_MAP` only converges using `fmin_bfgs` when starting with `y_sim` in the range 13-15 (the test point is 10). Whereas `fmin_powell` converges to the correct MAP for `y_sim>5` (though there is still some noise around 14). `basinhopping` always works, but because it has a slightly different interface requires a wrapper function. I can include this in the example.