poisson

I am new with poisson point process. I did one simluation (matlab) as below. My intensity lambda = 50 ; clear all; lambda=50; npoints = poissrnd(lambda); pproc = rand(npoints, 2); plot(pproc(:, 1), pproc(:, 2), '.'); Then I have plot, However, the link http://connor-johnson.com/2014/02/25/spatial-point-processes/ showed me that when intensity lamuda = 0.2, smaller than 1 , he got The link also showed the code in Python.Please check it. Here is my question, why intensity is smaller than 1 , he still can plot something here? If I let my code's lamda = 0.2, there will be no value to plot. I think

Original Question: I want to generate a Poisson process. If the number of arrivals by time t is N(t) and I have a Poisson distribution with parameter λ how do I generate N(t) ? How would I do this in C++? Clarification: I originally wanted to generate the process using a Poisson distribution. But, I was confused about what parameter from the process I needed; I thought I could use N(t) but that tells me how many arrivals have occurred on the interval (0,t] which wasn't what I wanted. So, then I thought I could use N(t2)-N(t1) to get the number of arrivals on the interval [t1,t2] . Since N(t)

I found a package 'bivpois' for R which evaluates a model for two related poisson processes (for example, the number of goals by the home and the away team in a soccer game). However, this package seems to no longer be useable in newer versions of R. Is there a reasonable way to modify the glm() function to do a similar process, or run this older package on my new version of R? I have found very little literature on these sorts of processes and have found very little in terms of easy implementation in other statistical packages like STATA. Any suggestions would be much appreciated. While CRAN

I am trying to understand how HashMap is implemented in Java. I decided that I will try to understand every line (of code and comments) from that class and obviously I faced resistance very soon. The following snippet is from HashMap class and talks about Poisson Distribution: Ideally, under random hashCodes, the frequency of nodes in bins follows a Poisson distribution (http://en.wikipedia.org/wiki/Poisson_distribution) with a parameter of about 0.5 on average for the default resizing threshold of 0.75, although with a large variance because of resizing granularity. Ignoring variance, the

I would like to create a function in Java that generates Poisson arrivals given the mean arrival rate (lambda) and the mean service rate (mu). In my example I have: 2,2 requests/day, in other words 2,2 arrivals/day and a mean service time of 108 hours. Considering that my program starts at t=0 minutes, I would like to create a function that returns arrivals[], which will contain, t1, t2, and a possible t3. T1,t2 and t3 are the instants (in minutes) during the day where this arrivals occur. I have the following restrictions: t1 < t2 < t3 < 1440 minutes (24 hours*60 minutes/hour) t2-t1 > 108

I try to fit my data to a poisson distribution: import seaborn as sns import scipy.stats as stats sns.distplot(x, kde = False, fit = stats.poisson) But I get this error: AttributeError: 'poisson_gen' object has no attribute 'fit' Other distribution (gamma, etc) de work well. The Poisson distribution (implemented in scipy as scipy.stats.poisson ) is a discrete distribution . The discrete distributions in scipy do not have a fit method. I'm not very familiar with the seaborn.distplot function, but it appears to assume that the data comes from a continuous distribution. If that is the case, then

I'm aware of Knuth's algorithm for generating random Poisson distributed numbers (below in Java) but how do I translate that into calling a method, generateEvent() , randomly over time? int poissonRandomNumber(int lambda) { double L = Math.exp(-lambda); int k = 0; double p = 1; do { k = k + 1; double u = Math.random(); p = p * u; } while (p > L); return k - 1; } Jay Elston If you are looking to simulate the inter-event arrival time, you want the exponential distribution. Take a look at Pseudorandom Number Generator - Exponential Distribution Your code would then look like this: // Note L == 1

When you use the POISSON function in Excel (or in OpenOffice Calc), it takes two arguments: an integer an 'average' number and returns a float. In Python (I tried RandomArray and NumPy) it returns an array of random poisson numbers. What I really want is the percentage that this event will occur (it is a constant number and the array has every time different numbers - so is it an average?). for example: print poisson(2.6,6) returns [1 3 3 0 1 3] (and every time I run it, it's different). The number I get from calc/excel is 3.19 ( POISSON(6,2.16,0)*100 ). Am I using the python's poisson wrong

Imagine that I have a set of measurements of x that are taken by many processes x 0 ... x N at times t 0 ... t N . Let's assume that at time t I want to make an estimate of the current value of x , based on the assumption that there is no long term trend I know about and that x can be predicted from an algorithm such as exponential smoothing. As we have many processes, and N can get very large, I can't store more than a few values (e.g. the previous state). One approach here would be to adapt the normal exponential smoothing algorithm. If samples are taken regularly, I would maintain an