Parameter Estimation

Parameter Estimation

Parameter estimation for ODE models, also known as dynamic data analysis, is provided by the DiffEq suite. Note these require that the problem is defined using a ParameterizedFunction.

Recommended Methods

The recommended method is to use build_loss_objective with the optimizer of your choice. This method can thus be paired with global optimizers from packages like BlackBoxOptim.jl or NLopt.jl which can be much less prone to finding local minima than local optimization methods. Also, it allows the user to define the cost function in the way they choose as a function loss(sol), and thus can fit using any cost function on the solution, making it applicable to fitting non-temporal data and other types of problems. Also, build_loss_objective works for all of the DEProblem types, allowing it to optimize parameters on ODEs, SDEs, DDEs, DAEs, etc.

However, this method requires repeated solution of the differential equation. If the data is temporal data, the most efficient method is the two_stage_method which does not require repeated solutions but is not as accurate. Usage of the two_stage_method should have a post-processing step which refines using a method like build_loss_objective.

Optimization-Based Methods


The two-stage method is a collocation method for estimating parameters without requiring repeated solving of the differential equation. It does so by determining a smoothed estimated trajectory of the data and optimizing the derivative function and the data's timepoints to match the derivatives of the smoothed trajectory. This method has less accuracy than other methods but is much faster, and is a good method to try first to get in the general "good parameter" region, to then finish using one of the other methods.

function two_stage_method(prob::DEProblem,tpoints,data;kernel= :Epanechnikov,
                          loss_func = L2DistLoss,mpg_autodiff = false,
                          verbose = false,verbose_steps = 100)


build_loss_objective builds an objective function to be used with Optim.jl and MathProgBase-associated solvers like NLopt.

function build_loss_objective(prob::DEProblem,alg,loss_func
                              mpg_autodiff = false,
                              verbose_opt = false,
                              verbose_steps = 100,
                              prob_generator = problem_new_parameters,

The first argument is the DEProblem to solve, and next is the alg to use. The alg must match the problem type, which can be any DEProblem (ODEs, SDEs, DAEs, DDEs, etc.). regularization defaults to nothing which has no regulariztion function. One can also choose verbose_opt and verbose_steps, which, in the optimization routines, will print the steps and the values at the steps every verbose_steps steps. mpg_autodiff uses autodifferentiation to define the derivative for the MathProgBase solver. The extra keyword arguments are passed to the differential equation solver.

The Loss Function


is a function which reduces the problem's solution to a scalar which the optimizer will try to minimize. While this is very flexible, two convenience routines are included for fitting to data with standard cost functions:

CostVData(t,data;loss_func = L2Loss,weight=nothing)

where t is the set of timepoints which the data is found at, and data are the values that are known where each column corresponds to measures of the values of the system. L2Loss is an optimized version of the L2-distance. In CostVData, one can choose any loss function from LossFunctions.jl or use the default of an L2 loss. The weight is a vector of weights for the loss function which must match the size of the data. Note that minimization of a weighted L2Loss is equivalent to maximum likelihood estimation of a heteroskedastic Normally distributed likelihood.

Additionally, we include a more flexible log-likelihood approach:

LogLikeLoss(t,distributions;loss_func = L2Loss,weight=nothing)

In this case, there are two forms. The simple case is where distributions[i,j] is the likelihood distributions from a UnivariateDistribution from Distributions.jl, where it corresponds to the likelihood at t[i] for component j. The second case is where distributions[i] is a MultivariateDistribution which corresponds to the likelihood at t[i] over the vector of components. This likelihood function then calculates the negative of the total loglikelihood over time as its objective value (negative since optimizers generally find minimimums, and thus this corresponds to maximum likelihood estimation).

Note that these distributions can be generated via fit_mle on some dataset against some chosen distribution type.

Note About Loss Functions

For parameter estimation problems, it's not uncommon for the optimizers to hit unstable regions of parameter space. This causes warnings that the solver exited early, and the built-in loss functions like L2Loss and CostVData automatically handle this. However, if using a user-supplied loss function, you should make sure it's robust to these issues. One common pattern is to apply infinite loss when the integration is not successful. Using the retcodes, this can be done via:

function my_loss_function(sol)
   tot_loss = 0.0
   if any((s.retcode != :Success for s in sol))
     tot_loss = Inf
     # calculation for the loss here

The Regularization Function

The regularization can be any function of p, the parameter vector:


The Regularization helper function builds a regularization using a penalty function penalty from PenaltyFunctions.jl:


The regularization defaults to L2 if no penalty function is specified. λ is the weight parameter for the addition of the regularization term.

The Problem Generator Function

The argument prob_generator allows one to specify a function for generating new problems from a given parameter set. By default, this just builds a new version of f that inserts all of the parameters. For example, for ODEs this is given by the dispatch on DiffEqBase.problem_new_parameters which does the following:

function problem_new_parameters(prob::ODEProblem,p)
  f = (du,u,p,t) -> prob.f(du,u,p,t)
  uEltype = eltype(p)
  u0 = [uEltype(prob.u0[i]) for i in 1:length(prob.u0)]
  tspan = (uEltype(prob.tspan[1]),uEltype(prob.tspan[2]))

f = (t,u,du) -> prob.f(du,u,p,t) creates a new version of f that encloses the new parameters. The element types for u0 and tspan are set to match the parameters. This is required to make autodifferentiation work. Then the new problem with these new values is returned.

One can use this to change the meaning of the parameters using this function. For example, if one instead wanted to optimize the initial conditions for a function without parameters, you could change this to:

function my_problem_new_parameters(prob::ODEProblem,p)
  uEltype = eltype(p)
  tspan = (uEltype(prob.tspan[1]),uEltype(prob.tspan[2]))

which simply matches the type for time to p (once again, for autodifferentiation) and uses p as the initial condition in the initial value problem.


build_lsoptim_objective builds an objective function to be used with LeastSquaresOptim.jl.

build_lsoptim_objective(prob,tspan,t,data;prob_generator = problem_new_parameters,kwargs...)

The arguments are the same as build_loss_objective.


lm_fit is a function for fitting the parameters of an ODE using the Levenberg-Marquardt algorithm. This algorithm is really bad and thus not recommended since, for example, the Optim.jl algorithms on an L2 loss are more performant and robust. However, this is provided for completeness as most other differential equation libraries use an LM-based algorithm, so this allows one to test the increased effectiveness of not using LM.

lm_fit(prob::DEProblem,tspan,t,data,p0;prob_generator = problem_new_parameters,kwargs...)

The arguments are similar to before, but with p0 being the initial conditions for the parameters and the kwargs as the args passed to the LsqFit curve_fit function (which is used for the LM solver). This returns the fitted parameters.

Bayesian Methods

The following methods require the DiffEqBayes.jl

using DiffEqBayes


stan_inference(prob::ODEProblem,t,data,priors = nothing;alg=:rk45,
               num_samples=1000, num_warmup=1000, reltol=1e-3,
               abstol=1e-6, maxiter=Int(1e5),likelihood=Normal,

stan_inference uses Stan.jl to perform the Bayesian inference. The Stan installation process is required to use this function. The input requires that the function is defined by a ParameterizedFunction with the @ode_def macro. t is the array of time and data is the array where the first dimension (columns) corresponds to the array of system values. priors is an array of prior distributions for each parameter, specified via a Distributions.jl type. alg is a choice between :rk45 and :bdf, the two internal integrators of Stan. num_samples is the number of samples to take per chain, and num_warmup is the number of MCMC warmup steps. abstol and reltol are the keyword arguments for the internal integrator. liklihood is the likelihood distribution to use with the arguments from vars, and vars is a tuple of priors for the distributions of the likelihood hyperparameters. The special value StanODEData() in this tuple denotes the position that the ODE solution takes in the likelihood's parameter list.


turing_inference(prob::DEProblem,alg,t,data,priors = nothing;
                 num_samples=1000, epsilon = 0.02, tau = 4, kwargs...)

turing_inference uses Turing.jl to perform its parameter inference. prob can be any DEProblem with a corresponding alg choice. t is the array of time points and data[:,i] is the set of observations for the differential equation system at time point t[i] (or higher dimensional). priors is an array of prior distributions for each parameter, specified via a Distributions.jl type. num_samples is the number of samples per MCMC chain. epsilon and tau are the HMC parameters. The extra kwargs are given to the internal differential equation solver.


                      σ = 0.01,ϵ=0.001,initial=Float64[])

dynamichmc_inference uses DynamicHMC.jl to perform the bayesian parameter estimation. prob can be any DEProblem, data is the set of observations for our model whihc is to be used in the Bayesian Inference process. priors represent the choice of prior distributions for the parameters to be determined, passed as an array of Distributions.jl distributions. t is the array of time points. transformations is an array of Tranformations imposed for constraining the parameter values to specific domains. initial values for the parameters can be passed, if not passed the means of the priors are used. ϵ can be used as a kwarg to pass the initial step size for the NUTS algorithm.

Optimization-Based ODE Inference Examples

Simple Local Optimization

We choose to optimize the parameters on the Lotka-Volterra equation. We do so by defining the function as a ParameterizedFunction:

f = @ode_def LotkaVolterraTest begin
  dx = a*x - x*y
  dy = -3y + x*y
end a

u0 = [1.0;1.0]
tspan = (0.0,10.0)
p = [1.5]
prob = ODEProblem(f,u0,tspan,p)

We create data using the numerical result with a=1.5:

sol = solve(prob,Tsit5())
t = collect(linspace(0,10,200))
using RecursiveArrayTools # for VectorOfArray
randomized = VectorOfArray([(sol(t[i]) + .01randn(2)) for i in 1:length(t)])
data = convert(Array,randomized)

Here we used VectorOfArray from RecursiveArrayTools.jl to turn the result of an ODE into a matrix.

If we plot the solution with the parameter at a=1.42, we get the following:

Parameter Estimation Not Fit

Notice that after one period this solution begins to drift very far off: this problem is sensitive to the choice of a.

To build the objective function for Optim.jl, we simply call the build_loss_objective function:

cost_function = build_loss_objective(prob,Tsit5(),L2Loss(t,data),

This objective function internally is calling the ODE solver to get solutions to test against the data. The keyword arguments are passed directly to the solver. Note that we set maxiters in a way that causes the differential equation solvers to error more quickly when in bad regions of the parameter space, speeding up the process. If the integrator stops early (due to divergence), then those parameters are given an infinite loss, and thus this is a quick way to avoid bad parameters. We set verbose=false because this divergence can get noisy.

Before optimizing, let's visualize our cost function by plotting it for a range of parameter values:

range = 0.0:0.1:10.0
using Plots; plotly()
plot(range,[cost_function(i) for i in range],yscale=:log10,
     xaxis = "Parameter", yaxis = "Cost", title = "1-Parameter Cost Function",
     lw = 3)

1 Parameter Likelihood

Here we see that there is a very well-defined minimum in our cost function at the real parameter (because this is where the solution almost exactly fits the dataset).

Now this cost function can be used with Optim.jl in order to get the parameters. For example, we can use Brent's algorithm to search for the best solution on the interval [0,10] by:

using Optim
result = optimize(cost_function, 0.0, 10.0)

This returns result.minimizer[1]==1.5 as the best parameter to match the data. When we plot the fitted equation on the data, we receive the following:

Parameter Estimation Fit

Thus we see that after fitting, the lines match up with the generated data and receive the right parameter value.

We can also use the multivariate optimization functions. For example, we can use the BFGS algorithm to optimize the parameter starting at a=1.42 using:

result = optimize(cost_function, [1.42], BFGS())

Note that some of the algorithms may be sensitive to the initial condition. For more details on using Optim.jl, see the documentation for Optim.jl. We can improve our solution by noting that the Lotka-Volterra equation requires that the parameters are positive. Thus following the Optim.jl documentation we can add box constraints to ensure the optimizer only checks between 0.0 and 3.0 which improves the efficiency of our algorithm:

lower = [0.0]
upper = [3.0]
result = optimize(obj, [1.42], lower, upper, Fminbox{BFGS}())

Lastly, we can use the same tools to estimate multiple parameters simultaneously. Let's use the Lotka-Volterra equation with all parameters free:

f2 = @ode_def_nohes LotkaVolterraAll begin
  dx = a*x - b*x*y
  dy = -c*y + d*x*y
end a b c d

u0 = [1.0;1.0]
tspan = (0.0,10.0)
p = [1.5,1.0,3.0,1.0]
prob = ODEProblem(f2,u0,tspan,p)

We can build an objective function and solve the multiple parameter version just as before:

cost_function = build_loss_objective(prob,Tsit5(),CostVData(t,data),
result_bfgs = Optim.optimize(cost_function, [1.3,0.8,2.8,1.2], Optim.BFGS())

To solve it using LeastSquaresOptim.jl, we use the build_lsoptim_objective function:

cost_function = build_lsoptim_objective(prob1,t,data,Tsit5())

The result is a cost function which can be used with LeastSquaresOptim. For more details, consult the documentation for LeastSquaresOptim.jl:

x = [1.3,0.8,2.8,1.2]
res = optimize!(LeastSquaresProblem(x = x, f! = cost_function,
                output_length = length(t)*length(prob.u0)),

We can see the results are:


Results of Optimization Algorithm
 * Algorithm: Dogleg
 * Minimizer: [1.4995074428834114,0.9996531871795851,3.001556360700904,1.0006272074128821]
 * Sum of squares at Minimum: 0.035730
 * Iterations: 63
 * Convergence: true
 * |x - x'| < 1.0e-15: true
 * |f(x) - f(x')| / |f(x)| < 1.0e-14: false
 * |g(x)| < 1.0e-14: false
 * Function Calls: 64
 * Gradient Calls: 9
 * Multiplication Calls: 135

and thus this algorithm was able to correctly identify all four parameters.

More Algorithms (Global Optimization) via MathProgBase Solvers

The build_loss_objective function builds an objective function which is able to be used with MathProgBase-associated solvers. This includes packages like IPOPT, NLopt, MOSEK, etc. Building off of the previous example, we can build a cost function for the single parameter optimization problem like:

f = @ode_def_nohes LotkaVolterraTest begin
  dx = a*x - x*y
  dy = -3y + x*y
end a

u0 = [1.0;1.0]
tspan = (0.0,10.0)
p = [1.5]
prob = ODEProblem(f,u0,tspan,p)
sol = solve(prob,Tsit5())

t = collect(linspace(0,10,200))
randomized = VectorOfArray([(sol(t[i]) + .01randn(2)) for i in 1:length(t)])
data = convert(Array,randomized)

obj = build_loss_objective(prob,Tsit5(),L2Loss(t,data),maxiters=10000)

We can now use this obj as the objective function with MathProgBase solvers. For our example, we will use NLopt. To use the local derivative-free Constrained Optimization BY Linear Approximations algorithm, we can simply do:

using NLopt
opt = Opt(:LN_COBYLA, 1)
min_objective!(opt, obj)
(minf,minx,ret) = NLopt.optimize(opt,[1.3])

This finds a minimum at [1.49997]. For a modified evolutionary algorithm, we can use:

opt = Opt(:GN_ESCH, 1)
min_objective!(opt, obj)
maxeval!(opt, 100000)
(minf,minx,ret) = NLopt.optimize(opt,[1.3])

We can even use things like the Improved Stochastic Ranking Evolution Strategy (and add constraints if needed). This is done via:

opt = Opt(:GN_ISRES, 1)
min_objective!(opt, obj.cost_function2)
maxeval!(opt, 100000)
(minf,minx,ret) = NLopt.optimize(opt,[0.2])

which is very robust to the initial condition. The fastest result comes from the following:

using NLopt
opt = Opt(:LN_BOBYQA, 1)
min_objective!(opt, obj)
(minf,minx,ret) = NLopt.optimize(opt,[1.3])

For more information, see the NLopt documentation for more details. And give IPOPT or MOSEK a try!

Generalized Likelihood Example

In this example we will demo the likelihood-based approach to parameter fitting. First let's generate a dataset to fit. We will re-use the Lotka-Volterra equation but in this case fit just two parameters. Note that the parameter estimation tools do not require the use of the @ode_def macro, so let's demonstrate what the macro-less version looks like:

f1 = function (du,u,p,t)
  du[1] = p[1] * u[1] - p[2] * u[1]*u[2]
  du[2] = -3.0 * u[2] + u[1]*u[2]
p = [1.5,1.0]
u0 = [1.0;1.0]
tspan = (0.0,10.0)
prob1 = ODEProblem(f1,u0,tspan,p)
sol = solve(prob1,Tsit5())

This is a function with two parameters, [1.5,1.0] which generates the same ODE solution as before. This time, let's generate 100 datasets where at each point adds a little bit of randomness:

using RecursiveArrayTools # for VectorOfArray
t = collect(linspace(0,10,200))
function generate_data(sol,t)
  randomized = VectorOfArray([(sol(t[i]) + .01randn(2)) for i in 1:length(t)])
  data = convert(Array,randomized)
aggregate_data = convert(Array,VectorOfArray([generate_data(sol,t) for i in 1:100]))

here with t we measure the solution at 200 evenly spaced points. Thus aggregate_data is a 2x200x100 matrix where aggregate_data[i,j,k] is the ith component at time j of the kth dataset. What we first want to do is get a matrix of distributions where distributions[i,j] is the likelihood of component i at take j. We can do this via fit_mle on a chosen distributional form. For simplicity we choose the Normal distribution. aggregate_data[i,j,:] is the array of points at the given component and time, and thus we find the distribution parameters which fits best at each time point via:

using Distributions
distributions = [fit_mle(Normal,aggregate_data[i,j,:]) for i in 1:2, j in 1:200]

Notice for example that we have:

julia> distributions[1,1]
Distributions.Normal{Float64}(μ=1.0022440583676806, σ=0.009851964521952437)

that is, it fit the distribution to have its mean just about where our original solution was and the variance is about how much noise we added to the dataset. This this is a good check to see that the distributions we are trying to fit our parameters to makes sense.

Note that in this case the Normal distribution was a good choice, and in many cases it's a nice go-to choice, but one should experiment with other choices of distributions as well. For example, a TDist can be an interesting way to incorporate robustness to outliers since low degrees of free T-distributions act like Normal distributions but with longer tails (though fit_mle does not work with a T-distribution, you can get the means/variances and build appropriate distribution objects yourself).

Once we have the matrix of distributions, we can build the objective function corresponding to that distribution fit:

using DiffEqParamEstim
obj = build_loss_objective(prob1,Tsit5(),LogLikeLoss(t,distributions),

First let's use the objective function to plot the likelihood landscape:

using Plots; plotly()
range = 0.5:0.1:5.0
heatmap(range,range,[obj([j,i]) for i in range, j in range],
        yscale=:log10,xlabel="Parameter 1",ylabel="Parameter 2",
        title="Likelihood Landscape")

2 Parameter Likelihood

Recall that this is the negative loglikelihood and thus the minimum is the maximum of the likelihood. There is a clear valley where the second parameter is 1.5, while the first parameter's likelihood is more muddled. By taking a one-dimensional slice:

plot(range,[obj([i,1.0]) for i in range],lw=3,
     title="Parameter 1 Likelihood (Parameter 2 = 1.5)",
     xlabel = "Parameter 1", ylabel = "Objective Function Value")

1 Parmaeter Likelihood

we can see that there's still a clear minimum at the true value. Thus we will use the global optimizers from BlackBoxOptim.jl to find the values. We set our search range to be from 0.5 to 5.0 for both of the parameters and let it optimize:

using BlackBoxOptim
bound1 = Tuple{Float64, Float64}[(0.5, 5),(0.5, 5)]
result = bboptimize(obj;SearchRange = bound1, MaxSteps = 11e3)

Starting optimization with optimizer BlackBoxOptim.DiffEvoOpt{BlackBoxOptim.FitPopulation{Float64},B
0.00 secs, 0 evals, 0 steps
0.50 secs, 1972 evals, 1865 steps, improv/step: 0.266 (last = 0.2665), fitness=-737.311433781
1.00 secs, 3859 evals, 3753 steps, improv/step: 0.279 (last = 0.2913), fitness=-739.658421879
1.50 secs, 5904 evals, 5799 steps, improv/step: 0.280 (last = 0.2830), fitness=-739.658433715
2.00 secs, 7916 evals, 7811 steps, improv/step: 0.225 (last = 0.0646), fitness=-739.658433715
2.50 secs, 9966 evals, 9861 steps, improv/step: 0.183 (last = 0.0220), fitness=-739.658433715

Optimization stopped after 11001 steps and 2.7839999198913574 seconds
Termination reason: Max number of steps (11000) reached
Steps per second = 3951.50873439296
Function evals per second = 3989.2242527195904
Improvements/step = 0.165
Total function evaluations = 11106

Best candidate found: [1.50001, 1.00001]

Fitness: -739.658433715

This shows that it found the true parameters as the best fit to the likelihood.

Parameter Estimation for Stochastic Differential Equations and Monte Carlo

We can use any DEProblem, which not only includes DAEProblem and DDEProblems, but also stochastic problems. In this case, let's use the generalized maximum likelihood to fit the parameters of an SDE's Monte Carlo evaluation.

Let's use the same Lotka-Volterra equation as before, but this time add noise:

pf = function (du,u,p,t)
  du[1] = p[1] * u[1] - u[1]*u[2]
  du[2] = -3u[2] + u[1]*u[2]

u0 = [1.0;1.0]
pg = function (du,u,p,t)
  du[1] = p[2]*u[1]
  du[2] = 1e-2u[2]
p = [1.5,1e-2]
tspan = (0.0,10.0)
prob = SDEProblem(pf,pg,u0,tspan,p)

Now lets generate a dataset from 10,000 solutions of the SDE

using RecursiveArrayTools # for VectorOfArray
t = collect(linspace(0,10,200))
function generate_data(t)
  sol = solve(prob,SRIW1())
  randomized = VectorOfArray([(sol(t[i]) + .01randn(2)) for i in 1:length(t)])
  data = convert(Array,randomized)
aggregate_data = convert(Array,VectorOfArray([generate_data(t) for i in 1:10000]))

Instead of using UnivariateDistributions like in the previous example, lets fit our data to MultivariateNormal distributions.

using Distributions
distributions = [fit_mle(MultivariateNormal,aggregate_data[:,j,:]) for j in 1:200]

Now let's estimate the parameters. Instead of using single runs from the SDE, we will use a MonteCarloProblem. This means that it will solve the SDE N times to come up with an approximate probability distribution at each time point and use that in the likelihood estimate.

monte_prob = MonteCarloProblem(prob)
obj = build_loss_objective(monte_prob,SOSRI(),LogLikeLoss(t,distributions),
                                     maxiters=10000,verbose=false,num_monte = 1000,
                                     parallel_type = :threads)

To speed things up I enabled multithreading. Just as before, we hand this over to BlackBoxOptim.jl:

using BlackBoxOptim
bound1 = Tuple{Float64, Float64}[(0.5, 3),(1e-3, 1e-1)]
result = bboptimize(obj;SearchRange = bound1, MaxSteps = 400)

Optimization stopped after 201 steps and 2713
.5920000076294 seconds
Termination reason: Max number of steps (200)
Steps per second = 0.07407156271076672
Function evals per second = 0.106869418836429
Improvements/step = 0.42
Total function evaluations = 290

Best candidate found: [1.52075, 0.0216393]

Fitness: 1544423.794270536

Here we see that we successfully recovered the drift parameter, and got close to the original noise parameter after searching a two orders of magnitude range. It would require a larger num_monte to accurately get samples of the the variance and receive a better estimate there.

Bayesian Inference Examples


Like in the previous examples, we setup the Lotka-Volterra system and generate data:

f1 = @ode_def LotkaVolterraTest4 begin
  dx = a*x - b*x*y
  dy = -c*y + d*x*y
end a b c d
p = [1.5,1.0,3.0,1.0]
u0 = [1.0,1.0]
tspan = (0.0,10.0)
prob1 = ODEProblem(f1,u0,tspan,p)
sol = solve(prob1,Tsit5())
t = collect(linspace(1,10,10))
randomized = VectorOfArray([(sol(t[i]) + .01randn(2)) for i in 1:length(t)])
data = convert(Array,randomized)

Here we now give Stan an array of prior distributions for our parameters. Since the parameters of our differential equation must be positive, we utilize truncated Normal distributions to make sure that is satisfied in the result:

priors = [Truncated(Normal(1.5,0.1),0,2),Truncated(Normal(1.0,0.1),0,1.5),

We then give these to the inference function.

bayesian_result = stan_inference(prob1,t,data,priors;
                                 vars = (StanODEData(),InverseGamma(4,1)))

InverseGamma(4,1) is our starting estimation for the variance hyperparameter of the default Normal distribution. The result is a Mamba.jl chain object. We can pull out the parameter values via:

theta1 = bayesian_result.chain_results[:,["theta.1"],:]
theta2 = bayesian_result.chain_results[:,["theta.2"],:]
theta3 = bayesian_result.chain_results[:,["theta.3"],:]
theta4 = bayesian_result.chain_results[:,["theta.4"],:]

From these chains we can get our estimate for the parameters via:


We can get more of a description via:


# Result

Iterations = 1:100
Thinning interval = 1
Chains = 1,2,3,4
Samples per chain = 100

Empirical Posterior Estimates:
                  Mean         SD        Naive SE        MCSE         ESS    
         lp__ -6.15472697 1.657551334 0.08287756670 0.18425029767  80.9314979
accept_stat__  0.90165904 0.125913744 0.00629568721 0.02781181930  20.4968668
   stepsize__  0.68014975 0.112183047 0.00560915237 0.06468790087   3.0075188
  treedepth__  2.68750000 0.524911975 0.02624559875 0.10711170182  24.0159141
 n_leapfrog__  6.77000000 4.121841086 0.20609205428 0.18645821695 100.0000000
  divergent__  0.00000000 0.000000000 0.00000000000 0.00000000000         NaN
     energy__  9.12245750 2.518330231 0.12591651153 0.32894488320  58.6109941
     sigma1.1  0.57164997 0.128579363 0.00642896816 0.00444242658 100.0000000
     sigma1.2  0.58981422 0.131346442 0.00656732209 0.00397310122 100.0000000
       theta1  1.50237077 0.008234095 0.00041170473 0.00025803930 100.0000000
       theta2  0.99778276 0.009752574 0.00048762870 0.00009717115 100.0000000
       theta3  3.00087782 0.009619775 0.00048098873 0.00020301023 100.0000000
       theta4  0.99803569 0.008893244 0.00044466218 0.00040886528 100.0000000
      theta.1  1.50237077 0.008234095 0.00041170473 0.00025803930 100.0000000
      theta.2  0.99778276 0.009752574 0.00048762870 0.00009717115 100.0000000
      theta.3  3.00087782 0.009619775 0.00048098873 0.00020301023 100.0000000
      theta.4  0.99803569 0.008893244 0.00044466218 0.00040886528 100.0000000

                  2.5%        25.0%      50.0%      75.0%       97.5%   
         lp__ -10.11994750 -7.0569000 -5.8086150 -4.96936500 -3.81514375
accept_stat__   0.54808912  0.8624483  0.9472840  0.98695850  1.00000000
   stepsize__   0.57975100  0.5813920  0.6440120  0.74276975  0.85282400
  treedepth__   2.00000000  2.0000000  3.0000000  3.00000000  3.00000000
 n_leapfrog__   3.00000000  7.0000000  7.0000000  7.00000000 15.00000000
  divergent__   0.00000000  0.0000000  0.0000000  0.00000000  0.00000000
     energy__   5.54070300  7.2602200  8.7707000 10.74517500 14.91849500
     sigma1.1   0.38135240  0.4740865  0.5533195  0.64092575  0.89713635
     sigma1.2   0.39674703  0.4982615  0.5613655  0.66973025  0.88361407
       theta1   1.48728600  1.4967650  1.5022750  1.50805500  1.51931475
       theta2   0.97685115  0.9914630  0.9971435  1.00394250  1.01765575
       theta3   2.98354100  2.9937575  3.0001450  3.00819000  3.02065950
       theta4   0.97934128  0.9918495  0.9977415  1.00430750  1.01442975
      theta.1   1.48728600  1.4967650  1.5022750  1.50805500  1.51931475
      theta.2   0.97685115  0.9914630  0.9971435  1.00394250  1.01765575
      theta.3   2.98354100  2.9937575  3.0001450  3.00819000  3.02065950
      theta.4   0.97934128  0.9918495  0.9977415  1.00430750  1.01442975

More extensive information about the distributions is given by the plots:



This case we will build off of the Stan example. Note that turing_inference does not require the use of the @ode_def macro like Stan does, but it will still work with macro-defined functions. Thus, using the same setup as before, we simply give the setup to:

bayesian_result = turing_inference(prob,Tsit5(),t,data,priors;num_samples=500)

The chain for the ith parameter is then given by:


Summary statistics can be also be accessed:


The chain can be analysed by the trace plots and other plots obtained by:



We can use DynamicHMC.jl as the backend for sampling with the dynamic_inference function. It supports any DEProblem, priors can be passed as an array of Distributions.jl distributions, passing initial values is optional and in case where the user has a firm understanding of the domain the parameter values will lie in, tranformations can be used to pass an array of constraints for the parameters as an array of Transformations.

bayesian_result_hmc = dynamichmc_inference(prob1, data, [Normal(1.5, 1)], t, [bridge(ℝ, ℝ⁺, )])

A tuple with summary statistics and the chain values is returned. The chain for the ith parameter is given by:


For accessing the various summary statistics:


Some details about the NUTS sampler can be obtained from:


In case of dynamic_inference the trace plots for the ith parameter can be obtained by:


For a better idea of the summary statistics and plotting you can take a look at the benchmark notebooks