# Introducing the MonteCarlo Package

If func returns the deviation of the estimated parameter from its true value, then the table will represent the bias. This is easy, if the outcome is deterministic.

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Here is a piece of code for that:. First, the risk estimation is dependent of your sample of pieces of chicken. You can plot a histogram:. Here my 2 cents. When a Monte Carlo sample is simulated to be positive in the previous step, you can then simulate the ingested number of bacteria using the empirical distribution of your 20 values, or fit for example an exponential distribution.

In the latter case, have a look at the fitdistrplus package. In the former case, you could do it as follows:. Take care that you transfer your units correctly log values, sample weights, etc. Here my 2 cents as well! With the given information, the probability distribution of D can be derived as follows:.

For Pr D Contaminated a simple histogram suggests a Poisson like distribution. But you can fit another distribution. However, for count data it is customary to use Poisson or Binomial distributions. Once you have the distribution for D, you can sample from it and plugin in the risk of illness, R D.

This risk can be interpreted as the probability of been sick in one trial i. By clicking "Post Your Answer", you acknowledge that you have read our updated terms of service , privacy policy and cookie policy , and that your continued use of the website is subject to these policies.

Home Questions Tags Users Unanswered. Is my first step here to obtain the CDF? And what is the distribution I should use? I think you have to explain this a bit more, what exactly are you trying to do. An example or a sample of your data would be also nice. I am trying to firstly derive the probability distrubution for the risk R of a random person in GB becoming ill from eating a g portion of cooked chicken.

Here is a piece of code for that: You can plot a histogram: You could also fit a Beta distribution on these You can think of something like creating a super simple mobile application upgrade. Did you notice the difference between the two values? First we will try simple simulation of random values from 5 to 10 and apply it times. In the next test we will use the Monte Carlo Analysis with the Triangular distribution — it will use optimistic, pessimistic and most-likely values:.

From this graphic we can notice that the values which are occurring most frequently are between 7. The average of all the random values is 7.

Therefore our Mean is 7. As we tried different estimation techniques we found out that the result are approximately the same with slight difference, see below. Estimation Techniques Analogous estimating — we compare our current project with past similar ones. Three-point estimation — this technique considers the risk and uncertainty by taking in mind estimations for the optimistic, pessimistic and most-likely outcomes: Three-point estimation — PERT — variation of 3-point estimation where we have again the values for Optimistic, Pessimistic and Most-likely estimation.

The difference lies in the formula where we calculate the most-likely value with different weight. This is where the Monte Carlo Analysis comes in.

Monte Carlo Simulation Technique, used in project management where we can calculate random project estimations within a given range, as many times as we want. The result here for the Mean is 7. Conclusion As we tried different estimation techniques we found out that the result are approximately the same with slight difference, see below.