I have been looking into data fusion methods and what caught my eyes is the idea of using Kalman filter which looks into fusion data based on mean and variance of Gaussian distribution and implements the prediction and correction from weak sensor to stronger/more accurate sensor. I looked into the f…

Using the posterior mean to initialized the model for the next step should work in principle if the posterior is Gaussian like, which in this case, you would have some problem for parameter like nu and sigma. I dont have a out-of-the-box solution that I can recommend to you right now, as this is pre…

Did you mean that I would need to create equations in order to update the nu and sigma parameters? Also, how can I modify my model/code in order to work with for particle filter approach?

I was talking to someone about this concept and he mentioned doing bayesian updating. How can I implement this way into my code and using your package?

I was searching online and would I need to modify the functions similar to the question in stackflow? Updating model on PyMC3 with new observed data

Sorry for the late reply, I have been working on the data fusion for some time using a Bayesian filter. I am not sure if I am using Bayesian updating right, but, I got a function from the following link: Updating model on PyMC3 with new observed data However, when it reaches a stage of extracting …

I have been working on data fusion for some time using a Bayesian filter. I am not sure if I am using Bayesian updating right, but, I got a function from the following link: Updating model on PyMC3 with new observed data However, when it reaches the stage of extracting posterior information from i…

Creating a Data Fusion with PyMC3

Questions

junpenglao September 16, 2020, 6:17am 6

You can have a look at the discussion here: Performance speedup for updating posterior with new data

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Creating a Data Fusion with PyMC3

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