Unify the handling of different types of Ensemble Kalman methods

`EnsembleKalmanProcesses.jl` currently contains three Ensemble Kalman methods: Ensemble Kalman Inversion (EKI), Ensemble Kalman Sampling (EKS), and Unscented Kalman Inversion (UKI). Ideally, these three methods can be used through a common interface, which highlights their conceptual similarities while abstracting away their differences to the extent possible.

**Goal:**
The general idea is that an `EnsembleKalmanProcess` should be constructed from the observational mean, the covariance of the observational noise, and a `Process` struct (`Inversion`, `Sampler`, or `Unscented`), which contains the additional information that is needed for the particular process type. The construction of an `EnsembleKalmanProcess` should also include the generation of the initial ensemble of particles.

The signature of all `EnsembleKalmanProcess` outer constructors should thus look as follows:
`EnsembleKalmanProcess(obs_mean::Array{Float64, 1}, obs_cov::Array{Float64, 2}, process::Process)`

**Current status:**
At the moment, there are two `EnsembleKalmanProcess` outer constructors: a constructor for `Inversion` and `Sampler` processes, and a constructor for `Unscented` processes. In EKI and EKS, the ensemble of initial particles is generated prior to the construction of an `EnsembleKalmanProcess`, and the initial ensemble is then passed as an argument to the constructor, whereas in UKI the initial ensemble is generated as part of the construction. Both constructors take a time step argument `\Delta t`, which actually remains unused in UKI (see issue #16).

**To do:**
The unified interface outlined above requires the following changes:

- [ ] Move the construction of the initial ensemble (done by `construct_initial_ensemble`) into the `EnsembleKalmanProcess` constructor for EKI and EKS
- [ ] `construct_initial_ensemble` takes the prior (a `ParameterDistribution` object) and the desired ensemble size `N_ens` as inputs, so these two pieces of information have to be added to the `Inversion` and `Sampler` structs.
- [ ] The prior mean and covariance currently stored in the `Sampler` struct can be removed, as this information can be derived from the prior distribution
- [ ] Remove the time step \Delta t from the `EnsembleKalmanProcess` constructors and add it to the `Inversion` and `Sampler` structs instead (the `Unscented` struct doesn't need a \Delta t)

- [ ] A nice additional feature to have would be a function `get_solution(ekp::EnsembleKalmanProcess)`, which returns the solution of the particular Ensemble Kalman process (e.g., the mean of the final iteration for EKI, or the mean and covariance for EKS).