Spectrometer Sensor and Bayesian Belief Network

27 January 2015

This example explains how a Bayesian Belief Network (BBN) is used to interpret sensor readings from a spectrometer.

1: Application windows and spectrometer.

Within the application a window with sequentially changing colors is displayed in the lower left corner, see Figure 1. A spectrometer hardware device with 3648 channels analyses the optical emission within a range between 200nm and 1300 nm. This measured spectra is shown in the main application window Spectra. As a further processing step the spectra is weighted according to the sensitivity functions of the human eye cones http://en.wikipedia.org/wiki/Cone_cell. This means that 3 values are calculated from the spectra representing the excitation state of the color sensitive cells of the human eye (S-cone (Small), M-cone (Medium) and L-cone (Large)). While the human brain uses this information to identify the associated color, these triple values are forwarded to the Bayesian Network via a WEB Service to do a similar color detection job. In the sense of the Bayesian Network this means to calculate the probabilities to be in one of 16 color states. The returned probability values are finally shown on-line within the application Color recognition to verify the identification performance of the Bayesian Network.

Figure 2 below shows the structure of the BBN used in the example to represent the uncertainty in the domain and to support reasoning with uncertainty. The BBN has four variables where the variable Color is discrete with one state for each possible color while variables S-cone, M-cone, and L-cone are continuous variables with a Gaussian distribution conditional on Color.

2: Example BBN to interpret the sensor readings from a spectrometer.

The parameters of the BBN have estimated from a data sample. Figure 3 shows the prior distribution of each variable in the BBN. Each continuous variable has a marginal distribution which is a mixture of Gaussian distributions. The Color variable has an (approximately) uniform distribution over the colors.

3: Initial distributions in the BBN.

Figure 4 shows the posterior distribution of each variable in the BBN given knowledget hat _S-cone_ has value 0.95. Under this observation the most likely state is blue while fuchsia and aqua also have an increased posterior probability under this evidence.

4: Distributions when an observation that S-cone has value 0.95 is entered.

Below is a set of HUGIN widgets for interacting with the model (click on the probability bar to instantiate a node or remove evidence):

In the tree input fields below, then user can enter an observed numerical for the S-cone, M-cone, and L-cone. The values are used by the Bayesian Belief network to compute a probability distribution over the different colors represented in the model (right hand side).



Enter a value






This is a video demonstrating the integration between the sensor and the BBN module. The BBN module is implemented as a remote web service.

Contact information

For further details contact:
on BBNs: Anders L Madsen at anders-at-hugin-dot-com
on Spectrometer: Knut Voigtlaender at voigtlaender-at-adp-hyphen-dresden-dot-de


Kjærulff, U.B and Madsen, A.L. (2013): Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis. Second Edition. Springer.