Includes specified US style date format.

Changes:

1. Dates are pulled out of CovidUKStochastic
2. CovidUKStochastic now behaves more like a tfd.Distribution
    * CovidUKStochastic is now instantiated with an initial time, number of time steps and
time step size
    * CovidUKStochastic is now instantiated with the initial state.

Consider an SEIR model.  For adding $x \geq 0$ S->E event times we have:
\begin{equation}
S(t+1) &=& S(0) - (N_{se}(t) + x) \geq 0 \\
E(t+1) &=& E(0) + (N_{se}(t) + x) \geq 0
\end{equation}
such that $x$ is bounded by
$$
x \leq S(0) - N_{se}(t).
$$

Similarly for adding $x \geq 0$ E->I event times we have:
$$
x \leq E(0) + N_{se}(t) - N_{ei}(t).
$$

For deleting $x \geq 0$ S->E event times we have:
$$
x \leq E(0) + N_{se}(t) - N_{ei}(t)
$$
and for E->I event times we have;
$$
x \leq I(0) + N_{ei}(t) - N_{ir}(t).
$$

Changes:

1. Previously, we initialised from the first imputed event at T-s, where s was random due
to the initialisation process;

2. We now calculate the state at time T given s (and the event imputation).

Changes:

1. We adopt the convention [start, end) for *all* date ranges.
2. Modified PHE case ingestor to reflect this
3. Modified CovidUK to reflect this
4. Corrected a bug in the use of time in simulation.

Introduced counting process respecting AddOccultsProposal.  This now check to make sure
we don't invalidate the epidemic by adding too many events.  To achieve this, we combine
occults and POEs into 1 structure.  This has the advantage of requiring less storage, but
is less easy to monitor occults separately from POEs.

Added simple kernel wrapper for fixing parts of the cahin without having to modify the chain output code.

Changes:

1. Implemented unittest
2. Removed tf.print statement

When initiating occults from a previous posterior sample, it
is often necessary to make sure that the strict ordering of
events is adhered to. `regularize_occults` performs a recursive
adjustment of the occults to ensure that strict ordering remains intact.

Changes:

1. Bounded convergence parameter mis-specified in samplers (was 0.0, now 1.0)
2. Added 'name' property to GibbsStep

Changes:

1. Implemented GibbsStep and GibbsKernel classes
2. Modified mcmc.sample function to use Gibbs sampler
3. Amended bugs in event_time_mh.py and occult_proposal.py (edge cases where tf.gathers
were overshooting the bounds of the data stuctures, not apparent on a GPU but raised on
CPU).