10 views

Skip to first unread message

May 23, 2017, 8:42:20 AM5/23/17

to Stan users mailing list

Hey Stan Team and discussion participians,

one of my toy models for inferencing Markov Models of Ion channels Looks like this. (See below)

It works fine. (As long as I exclude probabilties = 0 or 1 for the binominal Distribution. The derivative of the log probabilty seems to be infinit there.?)

But in the real data I not going to see y_t but some y_gaus ~ normal(y_t, sigma). Are finitie mixtures here the right way to go? Are there better ways?

My data will be something like

p(t) = 1- exp(-theta*t)+....

n_open ~ Binomial(N_channel, p(t))

I ~ normal(Const * n_open, sigma)

I would like to infer p(t) via Theta (or more complex Sums of exponentials) and n_open and N_channel even though they are discrete. The const will probably come from different Data.

Thanks alot for any hints.

Jan Münch

data {

int<lower=1> N_data; // number of data points

int<lower=0, upper = 1000> y_t[N_data]; // array observations ever element bigger als 0 to 20 binomainal draw(20, theta)

real<lower=0, upper = 10> time[N_data];

int N_channel;

}

transformed data{ // ... declarations ... statements ...

}

parameters { // The parameters we want to inference by via Stan

simplex[2] mu;

real<lower = 0, upper = 5> theta[2];

}

transformed parameters { // ... declarations ... statements ...

real<lower = 0, upper = 1> probabilty[N]; // fitted values

for(i in 1:N)

probabilty[i] = 1 - mu[1] * exp(-theta[1] * time[i])- mu[2]* exp(-theta[2]*time[i]);

}

model {

theta[1] ~ uniform(0.0,3);

theta[2] ~ uniform(2.0,5);

y_t ~ binomial(N_channel, probabilty);

//y_gaus ~ normal(y_t,sigma);

}

generated quantities { // ... declarations ... statements ...

}

"""

Reply all

Reply to author

Forward

0 new messages

Search

Clear search

Close search

Google apps

Main menu