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
p=[20,-0.02,0.005,1.0]
prob = ODEProblem(f_Eprod,[0.0],[0.0,7],p)
sol=solve(prob)
```

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)
ODEProblem(prob.f,initial_conditions[i],tspan[i],[20,-0.02,0.005,IndVar[i],saveat=tspan[i][2])
end
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.