Hi

I want to use infer.NET for inferring networks from time series data. I have N variables, each can have a continuous value at time point t, which is a function of all node values at time (t-1).

Vi(t) = alphai * Vi(t-1) // this is the effect of the node's value in time t-1 on its value in time t

+ betai * 1 / (exp(sumj ( Wji * Vi(t) ))) // this is the effect of other nodes, stored in a matrix w for all nodes

alph and beta are parameters for tuning the amount of self/other's effect on current state of a node.

Sum is over all j values except i itself (j != i in sum).

w is the weight matrix. Wji is the weight of the effect of node j in time t-1 on node i in time t.

My first question is that can I implement this model in INFER.NET? the exponential part does not seem to be implementable.

If I have a simpler function for relating nodes in time t to time t-1, then how should I train this model to find weights (w). Should I just write the program, and then break my data into windows (t, t-1) records, and introduce this (t, t-1) data as the
observed data? How can I learn the weights?

Any help is appreciated.

Thanks a lot,

Zahra