I have been trying to estimate the MLE for my joint posterior. I'm using R and the package stats4. I have 14 parameters and two of them are $\geq 0$, which I did not know how to implement (and I was creating NaN due to the minus log posterior required in for the mle function) and I just made it return very high value (1000) if either of the parameters were negative. Is this the right way to solve this problem? As I was forced to change my prior each time (because MLE told me that my prior estimates were way to high) and I find these nonegative parameters going down to were low numbers (0.001 and 0.01) which did not seem right and at each iteration way below my repeatly suggested prior.

Also, since I didn't have the exact posterior due to the structure of the model and I tried to scale it such that the point estimate from the mle function plugged in the log joint posterior had the value 0. Is this approximation okay for this function?

Regards, Raxel.

I have been trying to estimate the MLE for my joint posterior. I'm using R and the package stats4. I have 14 parameters and two of them are $\geq 0$, which I did not know how to implement (and I was creating NaN due to the minus log posterior required in for the mle function) and I just made it return very high value (1000) if either of the parameters were negative. Is this the right way to solve this problem? As I was forced to change my prior each time (because MLE told me that my prior estimates were way to high) and I find these nonnegative parameters going down to were low numbers (0.001 and 0.01) which did not seem right and at each iteration way below my suggested prior.

Also, since I didn't have the exact posterior due to the structure of the model and I tried to scale it such that the point estimate from the mle function plugged in the log joint posterior had the value 0. Is this approximation okay for this function?

I have been trying to estimate the MLE for my joint posterior. I'm using R and the package stats4. I have 14 parameters and two of them are $\geq 0$, which I did not know how to implement (and I was creating NaN due to the minus log posterior required in for the mle function) and I just made it return very high vale (1000) if either of the parameters were negative. Is this the right way to solve this problem? As I was forced to change my prior each time (because MLE told me that my prior estimates were way to high) and I find these nonegative parameters going down to were low numbers (0.001 and 0.01) which did not seem right and at each iteration way below my repeatly suggested prior.

Also, since I didn't have the exact posterior due to the structure of the model and I tried to scale it such that the point estimate from the mle function plugged in the log joint posterior had the value 0. Is this approximation okay for this function?

Regards, Raxel.

I have been trying to estimate the MLE for my joint posterior. I'm using R and the package stats4. I have 14 parameters and two of them are $\geq 0$, which I did not know how to implement (and I was creating NaN due to the minus log posterior required in for the mle function) and I just made it return very high value (1000) if either of the parameters were negative. Is this the right way to solve this problem? As I was forced to change my prior each time (because MLE told me that my prior estimates were way to high) and I find these nonegative parameters going down to were low numbers (0.001 and 0.01) which did not seem right and at each iteration way below my repeatly suggested prior.

Also, since I didn't have the exact posterior due to the structure of the model and I tried to scale it such that the point estimate from the mle function plugged in the log joint posterior had the value 0. Is this approximation okay for this function?

Regards, Raxel.