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In <i>Kalman Folding, Part 1</i>, we present basic, static Kalman filtering
as a functional fold, highlighting the unique advantages of this form for
deploying test-hardened code verbatim in harsh, mission-critical environments.
In that paper, all examples folded over arrays in memory for convenience and
repeatability. That is an example of developing filters in a friendly
environment.
</p>

<p>
Here, we prototype a couple of less friendly environments and demonstrate
exactly the same Kalman accumulator function at work. These less friendly
environments are
</p>
<ul>
<li>lazy streams, where new observations are computed on demand but never fully
realized in memory, thus not available for inspection in a debugger</li>
<li>asynchronous observables, where new observations are delivered at arbitrary
times from an external source, thus not available for replay once consumed by
the filter</li>
</ul>

<p>
Streams are a natural fit for integration of differential equations, which often
arise in applications. As such, they enable unique modularization for all kinds
of filters, including non-linear Extended Kalman Filters.
</p>

<p>
The fact that the Kalman accumulator function gives bit-for-bit identical
results in all cases gives us high confidence that code developed in friendly
environments will behave as intended in unfriendly environments. This level of
repeatability is available <i>only</i> because of functional decomposition, which
minimizes the coupling between the accumulator function and the environment and
makes it possible to deploy exactly the same code, without even recompilation,
in all environments.
</p>