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.

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.

Changes:

1. Brought UncalibratedLogRandomWalk up to speed with latest tfp changes (stateless seed)
2. Added thin to example_config.yaml
3. Added parameter 'xi' to model to allow time-varying infec rate
4. Added parameter 'xi' to overall MCMC scheme.

Changes:

1. Replaced 149 UTLAs with 315 LTLAs mixing matrix;
2. Wrote geometric initialisation for censored event times;
3. Modified data ingester to take PHE Anonymised Line Listing data.

Changes:

1. Added occult Metropolis Hastings update.
2. Factored out Categorical2 distribution for use by both event time move and occults.
3. Refactored mcmc.py script for HDF5 output purposes
4. Apply compression to HDF5 output file.

Moved construction of Markov transition matrix format from
model.make_hazard.h into discrete_markov_log_prob.  Achieved a 33%
increase in run speed.

Re-wrote discrete_markov_log_prob to batch the call to
tfd.Multinomial.log_prob to avoid lgamma time.

First pass implementation of data augmentation MCMC.  This is slow, buggy, and possibly not correct yet!  Use with caution!

Working event time update.  Not compatible with XLA due to tf.boolean_mask which depends on tf.where.

Got stochastic model fitting with new bounded convergence respecting adaptive proposal.  However, XLA appears now very slow.

Performance approximation replacing matrix exponential with row-wise independent draws, a la chain binomial.

1. chainbinom* file and methods renamed to discrete_markov* files and methods.

2. Implemented batched hazard rate calculation, adding functions to automatically broadcast rates to the Markov transition rate matrix.

3. Implemented test simulation and MCMC algorithm.

1. Bugfix in initial condition setting for MCMC and prediction.

2. Rationalised data import.  Now uses a load_data function in the covid.model module.

3. Updated model doc to reflect loss of background infectious pressure, implementation of commuting frequency.

4. Fixes in covid_ode.py reflecting updated model interface.

Updated ODE model with overall number of starting individuals parameter, and daily confirmed cases up to 27th March (PHE, whole of England).

Big changes:
 1. replace python for loop with tf.while_loop
 2. work with a transposed state tensor shape
   - instead of [4, nlads * nages], use [nlads * nages, 4]
   - this made it pretty easy to eliminate some transposes in
     propagate_fn (there were comments there seemingly contemplating
     this shape arrangement)
   - this feels a little more natural to me, too; in TFP we'd call the 4
     SEIR states components of the "event shape" of the system, and the
     nlads * nages part a "batch shape" (although one could reasonably
     also combine these together into one big matrix "event shape")
   - anyway, this allowed elimination of 3 transpose ops which makes for
     simpler code and avoids some memcpys
   - I also made an effort to update surrounding code to use the same data
     layout, but it seems like mcmc.py and covid_ode.py are broken right
     now anyway, due to other changes made in support of stochastic mode,
     so I couldn't confirm that my changes were sufficient.
 3. switch off XLA (which didn't yield any clear improvement, although
    it also didn't really hurt), and disable autograph (which tries to
    do things like rewrite python for loops into TF graph code but tends
    to produce less performant than manually optimized code like what
    I've done here)

Wrote chainbinom_simulate.py function.  Halfway through re-writing the ODE class (which should now not be an ODE!).