# How do I propagate correlated errors numerically?

I'm facing an error propagation problem in fitting some experimental data. I have measured several quantities, $$m_i$$, and I know from theory that $$\sum_{i=0}^{n} m_i = 1$$. Each of the $$m_i$$ has its own measurement error, $$\delta_i$$ reported as a standard deviation of $$m_i$$. The covariance $$\sigma_{ij}$$ of any two $$m_i, m_j$$ can be calculated from experimental data; we'll assume they follow natural correlation (see comment).

I'm using the $$m_i$$ to derive another property, $$R_i$$. The transformation is complicated enough that I can't analytically propagate the errors in $$m_i$$. Instead, I hope to do so numerically: for $$N$$ cycles, I want to perturb the $$m_i$$ within measurement error and recalculate the $$R_i$$; I will save the calculated $$R_i$$ values and use these to calculate the errors in my products.

However, I'm not sure how to properly perturb the $$m_i$$ measurements, as they are correlated with each other. I've considered re-normalization (i.e. perturb all within error and then divide such that $$\sum_{i=0}^{n} m_i = 1$$). I've also considered using the covariances, in the following manner: suppose $$m_1 = 0.4 \pm 0.1$$, $$m_2 = 0.3 \pm 0.1$$, and $$m_3 = 0.3 \pm 0.1$$. I first perturb $$m_1$$ within error; maybe I get $$m_1 = 0.45$$. I then propagate this perturbation into $$m_2$$ and $$m_3$$; assuming equal covariances, I get $$m_1 = 0.45, m_2 = 0.275, m_3=0.275$$. I then proceed to perturb $$m_2$$, propagate based on covariance to satisfy the closure condition, then perturb $$m_3$$, and do the same.

But I don't have any theoretical justification for these ideas (or indeed formal training in statistics). What is a theoretically sound way to do this perturbation (or at least a reference that discusses this?)

• Can you tell us more about your covariances $\sigma_{ij}$. What do you mean they can be calculated from experiment data? If you don't consider the covariances, I think the normalization approach makes sense. May 12 '20 at 6:30
• By that I just meant that they are known values; i.e. one could pick a number for them and use it in the calculation. A reasonable assumption is that they are isotropic. By this I mean that $m_3 * \sigma_{12} = m_2 * \sigma_{13}$, that if I increase $m_1$ by an amount $x$ I decrease $m_2$ by $m2/(m2+m3) * x$ and $m3$ by $m3/(m2+m3) * x$. Is that the kind of statement you're looking for? Sorry if I've missed the question, a bit new to dealing with covariance! May 12 '20 at 17:55
• It seems the correlation you describe is the kind of "natural correlation" you would expect in any samples of proportions. Thus, if you simply sample and normalize, you shouldn't need to do anything. However, come to think of it, the normalization approach does have drawbacks. For example, what do you do if your sampled $m_i < 0$? Please clarify your question above and I'll try and give an answer below. May 13 '20 at 9:03

Random proportion vectors $$\boldsymbol{p}=(p_1, \ldots, p_n)$$ are usually negatively correlated. The usual way to sample proportions is to assume an underlying Dirichlet distribution. In your case, you may consider sampling from $$Dir(N\boldsymbol{m})$$ where $$\boldsymbol{m}$$ are the observed proportions and $$N$$ is a constant you choose such that the standard deviation/variance match what you expect. The approach of sampling from $$m_i \pm \delta_i$$ and normalizing might work, except for the difficulty in choosing an appropriate distribution for sampling. For example, if we assume $$N(m_i, \delta_i^2)$$, then there is the possibility of obtaining a negative result.