DifferentialEquations and Parameter Estimation with Indicator Variables

Hi I am new to Julia, sorry for this basic question, I am doing some
parameter fitting, and would like to use a binary indicator variable (0/1)
to differentiate groups from my data. I have the following code:

     f_Eprod = @ode_def Eprod begin
         dE = p1*exp(t*(p2+p2d*Ind_Var))
     end p1 p2 p2d Ind_Var

    prob = ODEProblem(f_Eprod,[0.0],[0.0,7],p)

Below I substitute the value of 1 in the parameter array by IndVar[i] to account for
grouping in the data. This array contains 0 and 1’s.

   function prob_func(prob,i,repeat)

   monte_prob = MonteCarloProblem(prob,prob_func=prob_func)

After setting a loss function, I am not sure how to set Optim.optimizer to avoid having Ind_Var being treated as a parameter to be optimized. Is there a way to handle this prior to the optimization? Thank you.

You’d make a loss function of 3 variables and in the loss function append IndVar and solve.

Hi Chris, thank you for your answer, I am a bit lost on how to implement this loss function you mention, would you provide some hints or point me to an example. Thank you in advance for any help.


Thank you