Hi, I have worked through the basic Pymc3 tutorials, but now I need some help building something a little bit more sophisticated. I’d like to produce posterior distributions on three variables that are combined to produce a single observable parameter in the following way:
Using the value of one of the variables, I calculate the upper limit of integration by solving a polynomial equation via the bisection method.
I numerically integrate a function that is independent of the variables from 0 to the calculated upper limit.
I multiply the result by powers of each of the variables to produce a rate of observations.
I make 0, 1, 2, … draws from a Poisson distribution with shape parameter of rate*time
In this way I hope to constrain the three variables that make up the rate. How do I implement this second step with pymc3 distributions?
Thanks, I’ll try to implement that and see what problems I run into. Any clue for computing the limit of integration? It amounts to a root-finding problem where the input variable changes the root (and so changes the limit of integration).
Any clue for computing the limit of integration? It amounts to a root-finding problem where the input variable changes the root (and so changes the limit of integration).
I wrote an implementation of newton’s method in Aesara for a project, you might find it helpful. Basically you need to write a function for one step of your optimizer, then use a scan together with an until condition.
@junpenglao i’m having some serious problems getting theano working - is there an installation guide somwhere? Following the steps on the theano sphinx page isn’t working and others’ advice is contradictory. Sorry that this isn’t really on-topic.