In ecology specific species can be used to get an indication/estimation of the environmental conditions. A simplistic example, lets assume I find a pike (Esox lucius). We know this species does not occur in sea water and only in fresh-brackish water. So, if we find this species, the location where this species occurred was fresh-brackish water.
I have a dataset with 62 aquatic plants. I grouped these plants on their sensitive to higher flow velocities (threshold >0.3-0.5 m s-1). I generalized that there is a probability of 65% that a given sensitive species is more likely to occur under lower flow velocities (threshold
If applicable sure. you might start here: Bayesian statistics for Environmental research - Bing Good luck, D. Booth
First, 60% should be prevalence/prior/base rate of low flow velocity. Then, your calculation fits into Bayes formula, that you have used.
Second, the prior distribution must be weak enough, not to overwhelm the data you have. Also it should not be very uninformative, to get overwhelmed by data.
Cheers
Somenath
Somenath Chatterjee What do you mean by "weak enough", "not to overwhelm the data" and "very uninformative"?
Wim Kaijser
Assume the prior P(A) is 0.9, which is very strong.
Conditional probability P(B/A) = 0.1, from fairly good amount of data.
The posterior P(A/B) = (0.1*0.9)/(0.1*0.9+0.9*0.1)=0.5, which is far away from what significant amount of data is saying. Here, the strong prior is overwhelming the information from data.
Assume the prior P(A) is 0.5, which is uninformative.
Conditional probability P(B/A) = 0.8, from a small data.
The posterior P(A/B) = (0.8*0.5)/(0.8*0.5+0.2*0.5)=0.8, which is what small data is saying. Here, uninformative prior is overwhelmed by strong and unstable conclusion by small data.
All the best
Somenath
- Badges
- Science topic