4. Measurement
This section adapts Definition 1 (http://planetmath.org/1introduction#Thmdefn1)to distributedstochastic systems. The first step is to replace elements ofstate space
with stochastic maps , or equivalently probability distributionson , which are the system’s inputs. Individualelements of correspond to Dirac distributions.
Second, replace function with mechanism.Since we are interested in the compositional structure ofmeasurements we also consider submechanisms .However, comparing mechanisms requires that they have the samedomain and range, so we extend to the entiresystem as follows
(1) |
We refer to the extension as by abuse of notation.We extend mechanisms implicitly whenever necessary withoutfurther comment. Extending mechanisms in this way maps thequale into a cloud of points in labeled by objects in .
In the special case of the initial object , define
Remark 3.
Subsystems differing by non-existent edges (Remark 2 (http://planetmath.org/3distributeddynamicalsystems#Thmrem2))are mapped to the same mechanism by this construction, thusmaking the fact that the edges do not exist explicit withinthe formalism.
Composing an input with a submechanism yields an output, which is a probability distribution on. We are now in a position to define
Definition 8.
A measuring device is the dual to the mechanism of a subsystem. An output is astochastic map . Ameasurement is a composition .
Recall that stochastic maps of the form correspond to probability distributions on . Outputs asdefined above are thus probability distributions on, the output alphabet of . Individualelements of are recovered as Dirac vectors:.
Definition 9.
The effective information generated by in the context of subsystem is
(2) |
The null context, corresponding to the empty subsystem, is a specialcase where is replaced by theuniform distribution on .To simplify notation define
Here, is theKullback-Leibler divergence or relative entropy[1]. Eq. (2) expands as
(3) |
When for some wehave
(4) |
Definition 8 requires some unpacking. To relateit to the classical notion of measurement, Definition 1 (http://planetmath.org/1introduction#Thmdefn1),we consider system where the alphabets of and are the sets and respectively, and themechanism of is . In other words,system corresponds to a single deterministic function.
Proposition 5 (classical measurement).
The measurement performed when deterministic function outputs is equivalent to the preimage
.Effective information is .
Proof:By Corollary 2 (http://planetmath.org/2stochasticmaps#Thmthm2)measurement is conditionaldistribution
which generalizes the preimage. Effective information followsimmediately.
Effective information can be interpreted as quantifying ameasurement’s precision. It is high if few inputs cause to output out of many – i.e. has fewelements relative to – and conversely is low if manyinputs cause to output – i.e. if the output isrelatively insensitive to changes in the input. Precisemeasurements say a lot about what the input could have beenand conversely for vague measurements with low .
The point of this paper is to develop techniques for studyingmeasurements constructed out of two or more functions. Wetherefore present computations for the simplest case,distributed system , in considerabledetail. Let be the graph

with obvious assignments of alphabets and the mechanism of as . To make the formulas more readablelet , and . We then obtain lattice

The remainder of this section and most of the next analyzesmeasurements in the lattice.
Proposition 6 (partial measurement).
The measurement performed on when outputs , treating as extrinsicnoise, is conditional distribution
(5) |
where . The effective information generated by thepartial measurement is
(6) |
Proof: Treating as a source of extrinsic noise yields which takes.The dual is
The computation of effective information follows immediately.
A partial measurement is precise if the preimage has small or empty intersection with for most, and large intersection for few .
Propositions 5 and 6 computeeffective information of a measurement relative to the nullcontext provided by complete
ignorance (the uniformdistribution). We can also compute the effective informationgenerated by a measurement in the context of a submeasurement:
Proposition 7 (relative measurement).
The information generated by measurement in the context of the partial measurement where is unobserved noise, is
(7) |
Proof: Applying Propositions 5 and6 obtains
which simplifies to the desired expression.
To interpret the result decompose into a family of functions labeled by elements of , where .The precision of the measurement performed by s . It follows thatthe precision of the relative measurement,Eq. (7), is the expected precision of themeasurements performed by family taken withrespect to the probability distribution generated bythe noisy measurement.
In the special case of relativeprecision is simply the difference of the precision of thelarger and smaller subsystems:
Corollary 8 (comparing measurements).
Proof: Applying Propositions 5,6, 7 and simplifying obtains
References
- 1 E T Jaynes (1985):Entropy
and Search Theory. In CR Smith & WT Grandy,editors: Maximum-entropy and Bayesian Methods inInverse Problems, Springer.