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For wearer protection, fit is particularly important because, as you inhale, you will be sucking air and floating particles in the air straight through any gaps. An even more economical option are shop towels. You can buy masks with Filti pockets at Amazon or Etsy.įilti can be sanitized with heat and re-used. Personally, I prefer to use Filtiwhich is a nanofiber material specifically designed for face masks. One piece of filter material makes hundreds of mask filters, so you can get together with your community to make lots from a single order. Based on this table, the clear winner appears to be Filtrete There are instructions available for creating masks with this material. A lower resistance is better-it is a measure of how hard it is to breath through. The droplets that you need to filter out to protect yourself when wearing a mask are smaller than those that you have to filter out to protect those around you.Ī higher efficiency is better-it shows the percentage of particles that were filtered remember, this is with much smaller particles at a much higher flow rate than we see in practice. For such situations, we can use a broader measure of uncertainty, such as evidence theory, and estimate belief/plausibility where belief and plausibility are, respectively, lower and upper bounds on probability.Unfortunately, many public health bodies still incorrectly claim that there is no evidence that DIY masks are useful at protecting the wearer.Įffective protection for the wearer of a mask depends on three critical things.
#XASH3D HALF LIFE PARTICLE UPDATE#
For example, using a Bayesian approach, if we have a poor prior, and little information to update to a posterior, the poor prior cannot be modified accurately to provide a good posterior and the Bayesian estimate can be way off. (More trials contribute to our improved state of knowledge, but in general other factors also contribute.)įor cases with significant state of knowledge (epistemic) uncertainty, we have insufficient information to use the probability measure of uncertainty, even in a Bayesian sense. Our uncertainty is reduced as we perform more trials or improve our state of knowledge the confidence interval is reduced and the subjective probability distribution is "narrowed". Using the Bayesian approach we can treat the classical $P_O$ as a random variable and express the uncertainty in $P_O$ as a subjective probability distribution for $P_O$ based on our imperfect state of knowledge. Using classical statistical inference this uncertainty can be expressed as a confidence interval for $P_O$.
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See the text Bayesian Reliability Analysis by Martz and Waller for information on the Bayesian approach.įor the more general case where we have limited information (trials for the classical case or state of knowledge for the Bayesian case) we have uncertainty in the probability. With sufficient information, the classical objective probability $P_O$ and the Bayesian subjective probability $P_S$ for the event are the same: one value with no uncertainty. For a large body of information we know the updated $P_S$ with no essentially uncertainty. For a very large $N$, observing $N(E)$, we know $P_O$ with essentially no uncertainty.įor the Bayesian approach, we assume a prior value for the event, $P_S$, and update it to a more accurate estimate for $P_S$, called the posterior, as we gather more information. This probability is the same using either a classical (objective or frequency) approach, or a Bayesian (subjective) approach.įor the classical approach, the probability of event $E$ is $P_O = lim_$ where $N$ is the number of independent trials and $N(E)$ is the number of times event $E$ occurs.
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We have sufficient information from observing the decay of a very large number of identical radionuclides to claim we know the decay rate, hence the probability of decay, with no uncertainty. The following is a little more discussion of the good comment by Karonen on the response.