I solved this problem by leaving out the tofit parameter. It doesn't skip the Markov Chain Monte Carlo procedure then and everything should work as supposed.

The Poisson distribution is a discrete probability distribution, meaning that you can only get integer variates, not decimal. Note: this doesn't mean the probability P associated with a particular variate is integer, that can be a decimal, just the individual variates themselves must be integer....

It's not very clearly explained in the documents, but there are a few prerequisites for using custom density functions: the function's name must start with d, must have first argument x, and have a named argument log in order to use additional methods such as predict you have to define...

python,numpy,random,noise,poisson

This is implicit in the algorithm to calculate the random sample of the poisson distribution. See the source code here. The random sample is calculated in a conditional loop, which gets a new random value and returns when this value is above some threshold based on lambda. For different lambda's...

algorithm,smoothing,exponential,poisson

First, if you assume that the occurrence rate of the events itself is constant (or that you're only interested in its long-term average), then you can simply estimate it as: λ* = N / (t − t0) where t is the current time, t0 is the...

c++,random,poisson,network-traffic

[Explanation about Poisson distribution which may help you for better understanding] The meaning of "Poisson distribution" and the function of "std::poisson_distribution()" are tightly related but not same. The Poisson distribution is a discrete probability distribution. You can calculate probabilities like probability of no packet comes in next period (one second...

matlab,permutation,probability,poisson,binomial-cdf

How about this? probability = [.3 .2 .4 .7]; n = numel(probability); combs = dec2bin(0:2^n-1).'-'0'; %'// each column is a combination of n values, %// where each value is either 0 or 1. A 1 value will represent an event %// that happens; a 0 value will represent an event...

r,datetime,data,survival-analysis,poisson

I hope this does what you need. I've used lubridate and dplyr because they make things easier but the same results could be achieved in base. There's no need to retain year_done or first_jan_done, these can be removed with %>% select(-year_done, -first_jan_done) but I thought I would leave them in...

c++,c++11,random,normal-distribution,poisson

You can center both distributions in a point that suits your needs. But if M is small, then the Poisson distribution has a 'fat tail', that is, the probability of getting a number above M is higher compared to the normal distribution. In the normal case, you can control this...

c++,distribution,factorial,poisson

One workaround to deal with large n's is calculating the distribution in the log domain: X = ((e^-lambda)*(lambda^n))/n! ln X = -lambda + n*ln(lambda) - Sum (ln(n)) return e^X ...

It sounds like you have 12 groups and 3 years for a total of 36 data points. For each year i you have a single value v[i] and 12 group-specific values lambda[1], lambda[2], ..., lambda[12]. You didn't really specify how each e is drawn, so let's assume there's a global...

srand seeds rand, not your std::default_random_engine. To seed this, use std::default_random_engine generator(std::random_device{}()); ^^^^^^^^^^^^^^^^^^^^^^ e.g., any seed can go here ...