I have several data points with error bars, and these error bars are different sizes for each of the data points. I'd like to fit a model function to them which has non-linear parameters, and be able to get error bars on the model parameters, ie. if my model is something like [itex]f(x) = A + B(t-t_c)^\alpha[/itex], I want to be able to get error bars on A, B, [itex]t_c[/itex] and [itex]\alpha[/itex].(adsbygoogle = window.adsbygoogle || []).push({});

I was thinking of using a Monte Carlo algorithm, but this seems like it might be unnecessarily slow. Unless someone has pointers on how to make good guesses of the model parameters for the Monte Carlo to reduce the amount of rejected moves. I've done this sort of thing with discrete variables before, but not continuous ones, so I'm a little confused on how to make good MC moves for a continuous variable when you don't know a priori what level of precision you need in the variable.

**Physics Forums - The Fusion of Science and Community**

Join Physics Forums Today!

The friendliest, high quality science and math community on the planet! Everyone who loves science is here!

The friendliest, high quality science and math community on the planet! Everyone who loves science is here!

# Fitting a curve to data with error bars on curve parameters

Loading...

Similar Threads - Fitting curve data | Date |
---|---|

A Weighting data points with fitted curve in Matlab | Aug 1, 2017 |

Curve fitting | Oct 18, 2015 |

Fitting experimental data with exponential curve | Jul 31, 2014 |

Curve-fitting to data with horizontal/vertical error bars | Oct 19, 2012 |

Fitted curve to measured data - statistical properties of the fit error | Feb 4, 2012 |

**Physics Forums - The Fusion of Science and Community**