# Simple functions¶

Bumps allows fits with varying levels of complexity. Simple fits accept a function $$f(x;p)$$ and data $$x,y,\sigma_y$$, where vector $$y$$ is the value measured in conditions $$x$$, and $$\sigma_y$$ is the $$1-\sigma$$ uncertainty in the measurement. Bumps also provides a simple wrapper for poisson data taken from counting statistics, with function $$f(x;p)$$ and data $$x,y$$. sim.py is a simulation of data from a poisson process, showing maximum likelihood, expected value and variance.

The ode2 example shows how to fit a system of coupled differential equations where multiple values are tracked at each time step.