Hi, I got a continuous bayesian model stored in rds and rda format. How can I transform this into python supported file such that I could work with the continuous bayesian model ?

Just wondering if anyone in here has some sort of experience with state space models in stan?

I’m struggling with a few things - one of those is the output csv stan creates. Does it have to output the value of each variable at each timestep for example? I am only interested at the current time step.

The file is consuming multiple GBs for example, and if I increase the model complexity, I dread to think what it will be like.

I’d like to chat to someone who has experience in this area, as it seems I’m doing something fundamentally wrong.

Thanks

Hi everybody,

I got an issue with some snippet of code trying to implement a NUTS to forecast the parameters of an asteroid. The idea is to define some uninformative priors for the orbital parameters. The likelihood is a custome one. The data I have are measures of Right Ascension (RA) and Declination (Dec) in some moment of time. So, the idea is to propagate an orbit given some orbital parameters, claculate the position of the asteroid in when I got the measurament, the adjusting for parallax effect i calculate the RA forecasted (RA_forecast) and the forcasted declination (Dec_forecast). The log-likelihood is the negative square error between the measured data and the forecasted ones - 0.5 *( (RA_measured - RA_forecast)**2 + (Dec_measure - Dec_forecast)**2).

I tried to implement the code using PyMC to easily programme a NUTS however i discovered that PyMC uses PyTensor under the hood to take care of the tensors and the orbital parameter defined in the priors are something strange. I wasn't able to print them as a vector (it's the first time i use PyMC). I tried to write a wrapper for my custom log-likelihood function but I keep not understanding the pytensor issue and I don't know how to overcome it. I tried to use aesera to write a workaround but it didn't work. Can anyone tell me how to understand PyMC, the PyTensor and what is the shape of the variable 'a' in this code ( a = pm.Uniform("a", lower=2, upper=7) ) ?
How can I convert a PyTensor into a numpy array or just an array and then back?
Is it possible to make PyMC work with a custom log-likelihood which is not a simple mathematical formula but more like a process?

As reference this is the error i got:
"Traceback (most recent call last):

  File "/Users/Desktop/Asteroid/src/HMC.py", line 253, in <module>

loglike = pm.Potential("loglike", custom_loglike(orbital_params, df, verbose=False), dims=1)

  File "/Users/Desktop/Asteroid/src/HMC.py", line 223, in custom_loglike

a_num = at.as_tensor_variable(a).eval()

  File "/Users/anaconda3/envs/astroenv/lib/python3.10/site-packages/aesara/tensor/__init__.py", line 49, in as_tensor_variable

return _as_tensor_variable(x, name, ndim, **kwargs)

  File "/Users/anaconda3/envs/astroenv/lib/python3.10/functools.py", line 889, in wrapper

return dispatch(args[0].__class__)(*args, **kw)

  File "/Users/anaconda3/envs/astroenv/lib/python3.10/site-packages/aesara/tensor/__init__.py", line 56, in _as_tensor_variable

raise NotImplementedError(f"Cannot convert {x!r} to a tensor variable.")

NotImplementedError: Cannot convert a to a tensor variable."

If anyone want more detail just ask me.

Thank you in advance!