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--- how_to_use_gbay [2020/06/05 12:44] – ecelio
+++ how_to_use_gbay [2023/04/21 15:30] (current) – external edit 127.0.0.1
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 When the input data represents **hard evidence** (we know the state of the node at each pixel, with 100% certainty, e.g. we know that the land cover of a pixel is forest), then the input raster has one band, where the value of each pixel corresponds to the number of a state of the node. For continuous nodes, the value of the pixel represents the real value (e.g. forest cover of 75%).
 When we use **soft evidence** (a probability distribution for each pixel), the input raster should have as many bands as the number of states of the corresponding node. Each band represents the probability of a state (e.g. the probability that the land cover of a pixel is forest). The values of all bands should sum up to 100.
+If soft evidence is given (to a root or intermediate node), the effect is identical to the Netica's "calibrate..." function (in the "Enter findings" menu).
 === 3.2 Vector ===
 When using vector data (zipped **.shp** files or **.gdb** ESRI geodatabase files), the input nodes are represented in the attribute table of the dataset.
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 In addition, for each target node, some metrics of the posterior probability are calculated and stored in an additional raster file called target_node_stats.tif. For discrete target nodes, this file contains one band with Shannon’s evenness index of the posterior probability distribution:
-J = H'/Hmax, where H' = - SUMi(pi*ln(pi) with pi=ni/N , Hmax = ln(N), pi is the probability of state i and N is the number of states.
+\(J = H'/H_{max}\), where  \(H' =-sum_{i=1}^N(p_i*log_2p_i)\), \(H_{max} = log_2(N)\), \(p_i\) is the probability of state i and N is the number of states.
-(Compared to Evenness Index: number of individuals N = number of species S, so S not used in formula)
+(We use \(log_2\) to reflect the common use in information theory.)
-//DEPRECATED:  J = H/Hmax, where H = i=1Npilog2pi , Hmax = log2(N), pi is the probability of state i and N is the number of states.//
 The index is a standardized measure of entropy (can be compared between nodes with different numbers of states) and expresses uncertainty. It has values between 0 and 1, where 1 denotes a uniform distribution between all possible states (maximum uncertainty), and 0 denotes complete certainty that the output node is in a specific state.