# How Optimistic Do You Want to Be? Bayesian Neural Network Regression with Prediction Errors

May 31, 2018 — Sjoerd Smit , Technical Consultant

Neural networks are very well known for their uses in machine learning, but can be used as well in other, more specialized topics, like regression. Many people would probably first associate regression with statistics, but let me show you the ways in which neural networks can be helpful in this field. They are especially useful if the data you’re interested in doesn’t follow an obvious underlying trend you can exploit, like in polynomial regression.

In a sense, you can view neural network regression as a kind of intermediary solution between true regression (where you have a fixed probabilistic model with some underlying parameters you need to find) and interpolation (where your goal is mostly to draw an eye-pleasing line between your data points). Neural networks can get you something from both worlds: the flexibility of interpolation and the ability to produce predictions with error bars like when you do regression.

For those of you who already know about neural networks, I can give a very brief hint as to how this works: you build a randomized neural network with dropout layers that you train like you normally would, but after training you don’t deactivate the dropout layers and keep using them to sample the network several times while making predictions to get a measure of the errors. Don’t worry if that sentence didn’t make sense to you yet, because I will explain all of this in more detail.

## Ordinary Neural Network Regression

To start, let’s do some basic neural network regression on the following data I made by taking points on a bell curve (e.g. the function ) and adding random numbers to it:

✕
exampleData = {{-1.8290606952826973`, 0.34220332868351117`}, {-0.6221091101205225`, 0.6029615713235724`}, {-1.2928624443456638`, 0.14264805848673934`}, {1.7383127604822395`, \ -0.09676233458358859`}, {2.701795903782372`, 0.1256597483577385`}, {1.7400006797156493`, 0.07503425036465608`}, {-0.6367237544480613`, 0.8371547667282598`}, {-2.482802633037993`, 0.04691691595492773`}, {0.9566109777301293`, 0.3860569423794188`}, {-2.551790012296368`, \ -0.037340684890464014`}, {0.6626176509888584`, 0.7670620756823968`}, {2.865357628008809`, -0.1120949485036743`}, \ {0.024445094773154707`, 1.3288343886644758`}, {-2.6538667331049197`, \ -0.005468132072381475`}, {1.1353110951218213`, 0.15366247144719652`}, {3.209853579579198`, 0.20621896435600656`}, {0.13992534568622972`, 0.8204487134187859`}, {2.4013110392840886`, \ -0.26232722849881523`}, {-2.1199290467312526`, 0.09261482926621102`}, {-2.210336371360782`, 0.02664895740254644`}, {0.33732886898809156`, 1.1701573388517288`}, {-2.2548343241910374`, \ -0.3576908508717164`}, {-1.4077788877461703`, 0.269393680956761`}, {3.210242875591371`, 0.21099679051999695`}, {0.7898064016052615`, 0.6198835029596128`}, {2.1835077887328893`, 0.08410415228550497`}, {0.008631687647122632`, 1.0501425654209409`}, {2.1792531502694334`, \ -0.11606480328877161`}, {-3.231947584552822`, -0.2359904673791076`}, \ {-0.7980615888830211`, 0.5151437742866803`}} plot = ListPlot[exampleData, PlotStyle -> Red] |

A regression neural network is basically a chain of alternating linear and nonlinear layers: the linear layers give your net a lot of free parameters to work with, while the nonlinear layers make sure that things don’t get boring. Common examples of nonlinear layers are the hyperbolic tangent, logistic sigmoid and the ramp function. For simplicity, I will stick with the `Ramp` nonlinearity, which simply puts kinks into straight lines (meaning that you get regressions that are piecewise linear):

✕
netRamp = NetChain[ {LinearLayer[100], Ramp, LinearLayer[100], Ramp, LinearLayer[]}, "Input" -> "Real", "Output" -> "Real" ]; trainedRamp = NetTrain[netRamp, <|"Input" -> exampleData[[All, 1]], "Output" -> exampleData[[All, 2]]|>, Method -> "ADAM", LossFunction -> MeanSquaredLossLayer[], TimeGoal -> 120, TargetDevice -> "GPU"]; Show[Plot[ trainedRamp[x], {x, -3.5, 3.5}, PlotLabel -> "Overtrained network"], plot, ImageSize -> Full, PlotRange -> All] |

As you can see, the network more or less just follows the points because it doesn’t understand the difference between the trend and the noise in the data. In the range above, the mix-up between trend and noise is particularly bad. The longer you train the network and the larger your linear layer, the stronger this effect will be. Obviously this is not what you want, since you’re really interested in fitting the trend of the data. Besides: if you really want to fit noise, you could just use interpolation instead. To prevent this overfitting of the data, you regularize (as explained in this tutorial) the network by using any or all of the following: a `ValidationSet`, regularization or a `DropoutLayer`. I will focus on the regularization coefficient and on dropout layers (in the next section you’ll see why), so let me briefly explain how they work:

- regularization is also commonly known in neural networks as weight decay. It’s a method that penalizes the network for using large weights, which prevents it from over-complicating the regression curve. The size of this penalty is scaled by a parameter called the regularization coefficient: the larger this coefficient, the simpler the network is forced to be.
- Dropout, on the other hand, randomizes the network during training to make sure that it can’t just fit the noise in the data. A network with dropout layers is also called a stochastic network (though dropout is not the only way people can make a network stochastic). The amount of randomness is controlled by the dropout probability (which is commonly ).

To get a feeling of how these two methods regularize the regression, I made the following parameter sweeps of and :

✕
log\[Lambda]List = Range[-5, -1, 1]; regularizedNets = NetTrain[ netRamp, <|"Input" -> exampleData[[All, 1]], "Output" -> exampleData[[All, 2]]|>, LossFunction -> MeanSquaredLossLayer[], Method -> {"ADAM", "L2Regularization" -> 10^#}, TimeGoal -> 20 ] & /@ log\[Lambda]List; With[{xvals = Range[-3.5, 3.5, 0.1]}, Show[ ListPlot[ TimeSeries[Transpose@Through[regularizedNets[xvals]], {xvals}, ValueDimensions -> Length[regularizedNets]], PlotLabel -> "\!\(\*SubscriptBox[\(L\), \(2\)]\)-regularized networks", Joined -> True, PlotLegends -> Map[StringForm["`1` = `2`", Subscript[\[Lambda], 2], HoldForm[10^#]] &, log\[Lambda]List] ], plot, ImageSize -> 450, PlotRange -> All ] ] |

✕
pDropoutList = {0.0001, 0.001, 0.01, 0.05, 0.1, 0.5}; dropoutNets = NetChain[ {LinearLayer[300], Ramp, DropoutLayer[#], LinearLayer[]}, "Input" -> "Real", "Output" -> "Real" ] & /@ pDropoutList; trainedDropoutNets = NetTrain[ #, <|"Input" -> exampleData[[All, 1]], "Output" -> exampleData[[All, 2]]|>, LossFunction -> MeanSquaredLossLayer[], Method -> {"ADAM"(*,"L2Regularization"\[Rule]10^#*)}, TimeGoal -> 20 ] & /@ dropoutNets; With[{xvals = Range[-3.5, 3.5, 0.1]}, Show[ ListPlot[ TimeSeries[Transpose@Through[trainedDropoutNets[xvals]], {xvals}, ValueDimensions -> Length[trainedDropoutNets]], PlotLabel -> "Dropout-regularized networks", Joined -> True, PlotLegends -> Map[StringForm["`1` = `2`", Subscript[p, drop], #] &, pDropoutList] ], plot, ImageSize -> 450, PlotRange -> All ] ] |

To summarize:

- Regular prediction neural networks (NNs) usually consist of an alternating linear layer and nonlinear layers.
- The network can be made more flexible by using linear layers of a higher dimension or by simply making the chain longer.
- Because your network is very flexible, you need to apply some form of regularization to make sure you’re not just fitting noise.

## Neural Network Regression with Error Bands

Both regularization methods mentioned previously were originally proposed as ad hoc solutions to the overfitting problem. However, recent work has shown that there are actually very good fundamental mathematical reasons why these methods work. Even more importantly, it has been shown that you can use them to do better than just produce a regression line! For those of you who are interested, I suggest reading this blog post by Yarin Gal. His thesis “Uncertainty in Deep Learning” is also well worth a look and is the main source for what follows in the rest of this post.

As it turns out, there is a link between stochastic regression neural networks and Gaussian processes, which are free-form regression methods that let you predict values and put error bands on those predictions. To do this, we need to consider neural network regression as a proper Bayesian inference procedure. Normally, Bayesian inference is quite computationally expensive, but as it conveniently turns out, you can do an approximate inference with minimal extra effort on top of what I already did above.

The basic idea is to use dropout layers to create a noisy neural network that is trained on the data as normal. However, I’m also going to use the dropout layers when doing predictions: for every value where I need a prediction, I will sample the network multiple times to get a sense of the errors in the predictions.

Furthermore, it’s good to keep in mind that you, as a newly converted Bayesian, are also dealing with priors. In particular, the network weights are now random variables with a prior distribution and a posterior distribution (i.e. the distributions before and after learning). This may sound rather difficult, so let me try to answer two questions you may have at this point:

**Q1**: Does that mean that I actually have to think hard about my prior now?

**A1**: No, not really, because it simply turns out that our old friend , the regularization coefficient, is really just the inverse standard deviation of the network prior weights: if you choose a larger , that means you’re only allowing small network weights.

**Q2**: So what about the posterior distribution of the weights? Don’t I have to integrate the predictions over the posterior weight distribution to get a posterior predictive distribution?

**A2**: Yes, you do, and that’s exactly what you do (at least approximately) when you sample the trained network with the dropout layers active. The sampling of the network is just a form of Monte Carlo integration over the posterior distribution.

So as you can see, being a Bayesian here really just means giving things a different name without having to change your way of doing things very much.

## Regression with a Constant Noise Level

Let’s start with the simplest type of regression in which the noise level of the data is assumed constant across the *x* axis. This is also called homoscedastic regression (as opposed to heteroscedastic regression, where the noise is a function of *x*). It does not, however, mean that the prediction error will also be constant: the prediction error depends on the noise level but also on the uncertainty in the network weights.

So let’s get to it and see how this works out, shall we? First I will define my network with a dropout layer. Normally you’d put a dropout layer before every linear layer, but since the input is just a number, I’m omitting the first dropout layer:

✕
\[Lambda]2 = 0.01; pdrop = 0.1; nUnits = 300; activation = Ramp; net = NetChain[ {LinearLayer[nUnits], ElementwiseLayer[activation], DropoutLayer[pdrop], LinearLayer[]}, "Input" -> "Real", "Output" -> "Real" ] |

✕
trainedNet = NetTrain[ net, <|"Input" -> exampleData[[All, 1]], "Output" -> exampleData[[All, 2]]|>, LossFunction -> MeanSquaredLossLayer[], Method -> {"ADAM", "L2Regularization" -> \[Lambda]2}, TimeGoal -> 10 ]; |

Next, we need to produce predictions from this model. To calibrate the model, you need to provide a prior length scale l that expresses your belief in how correlated the data is over a distance (just like in Gaussian process regression). Together with the regularization coefficient , the dropout probability p and the number of training data points N, you have to add the following variance to the sample variance of the network:

The following function takes a trained net and samples it multiple times with the dropout layers active (using `NetEvaluationMode → "Train"`). It then constructs a time series object of the –1, 0 and +1*σ* bands of the predictions:

✕
sampleNet[net : (_NetChain | _NetGraph), xvalues_List, sampleNumber_Integer?Positive, {lengthScale_, l2reg_, prob_, nExample_}] := TimeSeries[ Map[ With[{ mean = Mean[#], stdv = Sqrt[Variance[#] + (2 l2reg nExample)/(lengthScale^2 (1 - prob))] }, mean + stdv*{-1, 0, 1} ] &, Transpose@ Select[Table[ net[xvalues, NetEvaluationMode -> "Train"], {i, sampleNumber}], ListQ]], {xvalues}, ValueDimensions -> 3 ]; |

Now we can go ahead and plot the predictions with 1*σ* error bands. The prior seems to work reasonably well, though in real applications you’d need to calibrate it with a validation set (just like you would with and p).

✕
l = 2; samples = sampleNet[trainedNet, Range[-5, 5, 0.05], 200, {l, \[Lambda]2, pdrop, Length[exampleData]}]; Show[ ListPlot[ samples, Joined -> True, Filling -> {1 -> {2}, 3 -> {2}}, PlotStyle -> {Lighter[Blue], Blue, Lighter[Blue]} ], ListPlot[exampleData, PlotStyle -> Red], ImageSize -> 600, PlotRange -> All ] |

As you can see, the network has a tendency to do linear extrapolation due to my choice of the ramp nonlinearity. Picking different nonlinearities will lead to different extrapolation behaviors. In terms of Gaussian process regression, the choice of your network design influences the effective covariance kernel you’re using.

If you’re curious to see how the different network parameters influence the look of the regression, skip down a few paragraphs and try the manipulates, where you can interactively train your own network on data you can edit on the fly.

## Regression on Data with Varying Noise Levels

In heteroscedastic regression, you let the neural net try and find the noise level for itself. This means that the regression network outputs two numbers instead of one: a mean and a standard deviation. However, since the outputs of the network are real numbers, it’s easier if you use the log-precision instead of the standard deviation: :

✕
\[Lambda]2 = 0.01; pdrop = 0.1; nUnits = 300; activation = Ramp; regressionNet = NetGraph[ {LinearLayer[nUnits], ElementwiseLayer[activation], DropoutLayer[pdrop], LinearLayer[], LinearLayer[]}, { NetPort["Input"] -> 1 -> 2 -> 3, 3 -> 4 -> NetPort["Mean"], 3 -> 5 -> NetPort["LogPrecision"] }, "Input" -> "Real", "Mean" -> "Real", "LogPrecision" -> "Real" ] |

Next, instead of using a `MeanSquaredLossLayer` to train the network, you minimize the negative log-likelihood of the observed data. Again, you replace *σ* with the log of the precision and multiply everything by 2 to be in agreement with the convention of `MeanSquaredLossLayer`.

✕
FullSimplify[-2* LogLikelihood[ NormalDistribution[\[Mu], \[Sigma]], {yobs}] /. \[Sigma] -> 1/ Sqrt[Exp[log\[Tau]]], Assumptions -> log\[Tau] \[Element] Reals] |

Discarding the constant term gives us the following loss:

✕
loss = Function[{y, mean, logPrecision}, (y - mean)^2*Exp[logPrecision] - logPrecision ]; netHetero = NetGraph[<| "reg" -> regressionNet, "negLoglikelihood" -> ThreadingLayer[loss] |>, { NetPort["x"] -> "reg", {NetPort["y"], NetPort[{"reg", "Mean"}], NetPort[{"reg", "LogPrecision"}]} -> "negLoglikelihood" -> NetPort["Loss"] }, "y" -> "Real", "Loss" -> "Real" ] |

✕
trainedNetHetero = NetTrain[ netHetero, <|"x" -> exampleData[[All, 1]], "y" -> exampleData[[All, 2]]|>, LossFunction -> "Loss", Method -> {"ADAM", "L2Regularization" -> \[Lambda]2} ]; |

Again, the predictions are sampled multiple times. The predictive variance is now the sum of the variance of the predicted mean + mean of the predicted variance. The priors no longer influence the variance directly, but only through the network training:

✕
sampleNetHetero[net : (_NetChain | _NetGraph), xvalues_List, sampleNumber_Integer?Positive] := With[{regressionNet = NetExtract[net, "reg"]}, TimeSeries[ With[{ samples = Select[Table[ regressionNet[xvalues, NetEvaluationMode -> "Train"], {i, sampleNumber}], AssociationQ] }, With[{ mean = Mean[samples[[All, "Mean"]]], stdv = Sqrt[Variance[samples[[All, "Mean"]]] + Mean[Exp[-samples[[All, "LogPrecision"]]]]] }, Transpose[{mean - stdv, mean, mean + stdv}] ] ], {xvalues}, ValueDimensions -> 3 ] ]; |

Now you can plot the predictions with 1*σ* error bands:

✕
samples = sampleNetHetero[trainedNetHetero, Range[-5, 5, 0.05], 200]; Show[ ListPlot[ samples, Joined -> True, Filling -> {1 -> {2}, 3 -> {2}}, PlotStyle -> {Lighter[Blue], Blue, Lighter[Blue]} ], ListPlot[exampleData, PlotStyle -> Red], ImageSize -> 600, PlotRange -> All ] |

Of course, it’s still necessary to do validation of this network; one network architecture might be much better suited to the data at hand than another, so there is still the need to use validation sets to decide which model you have to use and with what parameters. Attached to the end of this blog post, you’ll find a notebook with an interactive demo of the regression method I just showed. With this code, you can find out for yourself how the different model parameters influence the predictions of the network.

## Optimistic or Pessimistic Error Bars? Introducing the Alpha-Divergence Loss Function

The code in this section shows how to implement the loss function described in the paper “Dropout Inference in Bayesian Neural Networks with Alpha-Divergences” by Li and Gal. For an interpretation of the α parameter used in this work, see e.g. figure 2 in “Black-Box α-Divergence Minimization” by Hernández-Lobato et al (2016).

In the paper by Li and Gal, the authors propose a modified loss function ℒ for a stochastic neural network to solve a weakness of the standard loss function I used above: it tends to underfit the posterior and give overly optimistic predictions. Optimistic predictions are a problem: when you fit your data to try and get a sense of what the real world might give you, you don’t want to be thrown a curveball afterwards.

During training, the training inputs (with indexing the training examples) are fed through the network K times to sample the outputs and compared to the training outputs . Given a particular standard loss function l (e.g. mean square error, negative log likelihood, cross-entropy) and regularization function for the weights *θ*, the modified loss function ℒ is given as:

The parameter *α* is the divergence parameter, which is typically tuned to (though you can pick other values as well, if you want). It can be thought of as a “pessimism” parameter: the higher it is, the more the network will tend to err on the side of caution and the larger error estimates. Practically speaking, a higher *α* parameter makes the loss function more lenient to the presence of large losses among the K samples, meaning that after training the network will produce a larger spread of predictions when sampled. Literature seems to suggest that is a pretty good value to start with. In the limit *α*→0, the LogSumExp simply becomes the sample average over K losses.

As can be seen, we need to sample the network several times during training. We can accomplish this with `NetMapOperator`. As a simple example, suppose we want to apply a dropout layer times to the same input. To do this, we duplicate the input and then wrap a `NetMapOperator` around the dropout layer and map it over the duplicated input:

✕
input = Range[5]; NetChain[{ ReplicateLayer[10], NetMapOperator[ DropoutLayer[0.5] ] } ][input, NetEvaluationMode -> "Train"] |

Next, define a net that will try to fit the data points with a normal distribution like in the previous heteroscedastic example. The output of the net is now a length-2 vector with the mean and the log precision (we can’t have two output ports because we’re going to have wrap the whole thing into `NetMapOperator`):

✕
alpha = 0.5; pdrop = 0.1; units = 300; activation = Ramp; \[Lambda]2 = 0.01; (*L2 regularization coefficient*) k = 25; (* number of samples of the network for calculating the loss*) regnet = NetInitialize@NetChain[{ LinearLayer[units], ElementwiseLayer[activation], DropoutLayer[pdrop], LinearLayer[] }, "Input" -> "Real", "Output" -> {2} ]; |

You will also need a network element to calculate the LogSumExp operator that aggregates the losses of the different samples of the regression network. I implemented the *α*-weighted `LogSumExp` by factoring out the largest term before feeding the vector into the exponent to make it more numerically stable. Note that I’m ignoring theterm since it’s a constant for the purpose of training the network.

✕
logsumexp\[Alpha][alpha_] := NetGraph[<| "timesAlpha" -> ElementwiseLayer[Function[-alpha #]], "max" -> AggregationLayer[Max, 1], "rep" -> ReplicateLayer[k], "sub" -> ThreadingLayer[Subtract], "expAlph" -> ElementwiseLayer[Exp], "sum" -> SummationLayer[], "logplusmax" -> ThreadingLayer[Function[{sum, max}, Log[sum] + max]], "invalpha" -> ElementwiseLayer[Function[-(#/alpha)]] |>, { NetPort["Input"] -> "timesAlpha", "timesAlpha" -> "max" -> "rep", {"timesAlpha", "rep"} -> "sub" -> "expAlph" -> "sum" , {"sum", "max"} -> "logplusmax" -> "invalpha" }, "Input" -> {k} ]; logsumexp\[Alpha][alpha] |

Define the network that will be used for training:

✕
net\[Alpha][alpha_] := NetGraph[<| "rep1" -> ReplicateLayer[k],(* replicate the inputs and outputs of the network *) "rep2" -> ReplicateLayer[k], "map" -> NetMapOperator[regnet], "mean" -> PartLayer[{All, 1}], "logprecision" -> PartLayer[{All, 2}], "loss" -> ThreadingLayer[ Function[{mean, logprecision, y}, (mean - y)^2*Exp[logprecision] - logprecision]], "logsumexp" -> logsumexp\[Alpha][alpha] |>, { NetPort["x"] -> "rep1" -> "map", "map" -> "mean", "map" -> "logprecision", NetPort["y"] -> "rep2", {"mean", "logprecision", "rep2"} -> "loss" -> "logsumexp" -> NetPort["Loss"] }, "x" -> "Real", "y" -> "Real" ]; net\[Alpha][alpha] |

… and train it:

✕
trainedNet\[Alpha] = NetTrain[ net\[Alpha][alpha], <|"x" -> exampleData[[All, 1]], "y" -> exampleData[[All, 2]]|>, LossFunction -> "Loss", Method -> {"ADAM", "L2Regularization" -> \[Lambda]2}, TargetDevice -> "CPU", TimeGoal -> 60 ]; |

✕
sampleNet\[Alpha][net : (_NetChain | _NetGraph), xvalues_List, nSamples_Integer?Positive] := With[{regnet = NetExtract[net, {"map", "Net"}]}, TimeSeries[ Map[ With[{ mean = Mean[#[[All, 1]]], stdv = Sqrt[Variance[#[[All, 1]]] + Mean[Exp[-#[[All, 2]]]]] }, mean + stdv*{-1, 0, 1} ] &, Transpose@Select[ Table[ regnet[xvalues, NetEvaluationMode -> "Train"], {i, nSamples} ], ListQ]], {xvalues}, ValueDimensions -> 3 ] ]; |

✕
samples = sampleNet\[Alpha][trainedNet\[Alpha], Range[-5, 5, 0.05], 200]; Show[ ListPlot[ samples, Joined -> True, Filling -> {1 -> {2}, 3 -> {2}}, PlotStyle -> {Lighter[Blue], Blue, Lighter[Blue]} ], ListPlot[exampleData, PlotStyle -> Red], ImageSize -> 600, PlotRange -> All ] |

I’ve discussed that dropout layers and the regularization coefficient in neural network training can actually be seen as components of a Bayesian inference procedure that approximates Gaussian process regression. By simply training a network with dropout layers like normal and then running the network several times in `NetEvaluationMode → "Train"`, you can get an estimate of the predictive posterior distribution, which not only includes the noise inherently in the data but also the uncertainty in the trained network weights.

If you’d like to learn more about this material or have any questions you’d like to ask, please feel free to visit my discussion on Wolfram Community.

*Download interactive examples from this post in a Wolfram Notebook.*

## 7 Comments

good

Thank you. Very interesting & useful….

This is very interesting and the method adapts quite naturally to different dimensions of data, thanks for posting this.

I am working on a problem with timeseries data (exhibiting serial dependency). What advice can you offer on using a similar approach with an LSTM based RNN (or GRU)?

The subject is touched on in Gal’s thesis (it appears feasible) however there are quite a lot of implementation considerations.

That’s a very good question. I noticed the section about recurrent neural networks in Yarin Gal’s thesis as well and I also have an interest in sequential regression problems. Generally, it’s easier to find information about recurrent networks by looking at classification problems, because that’s what most people are interested in. Ultimately, regression and classification aren’t that different, so I just tend to try translate between the two fields.

As an example, I recently found a StackExchange post by Sebastian about translation problems which gives a good baseline network to start thinking about recurrent regression problems:

https://mathematica.stackexchange.com/a/143672/43522

The documentation also has a good body of resources about sequence learning:

http://reference.wolfram.com/language/tutorial/NeuralNetworksSequenceLearning.html

The two main recurrent layers in WL (LongShortTermMemoryLayer and GatedRecurrentLayer) both support dropout, so once you have a regression network it should be fairly easy to use the quasi-Bayesian methods I described in my post. As it happens, I recently tried this out and I plan to make a post on the Wolfram community about it, so be sure to check for new posts there in the next few days.

Finally, if you’re interested in timeseries analysis, I also have a GitHub repository for Bayesian inference. The example notebook there has a section about timeseries modelling with stochastic differential equations. Feel free to check it out:

https://github.com/ssmit1986/BayesianInference

@Sjoerd I just noticed your post on the community on this – very impressive that you managed to do all that since yesterday! I guess another advert for the Wolfram neural net framework.

I had already downloaded the Bayesian inference package, it’s very nicely developed, I was intrigued by the stochastic differential equation example but struggled to find a theoretical justification for using it on the application I had (i.e. why this model rather another like a Gaussian Process for example).

Thanks for following up on my question, lots of interesting reading and experimentation ahead…

@Steve: Glad to hear you like it. The reason for using a stochastic differential equation was quite simple: mostly just for demonstration. I know that some people are interested in these kinds of fits and I wanted to show how to do something like EstimatedProcess (https://reference.wolfram.com/language/ref/EstimatedProcess.html) with my code and how to do it efficiently.

Furthermore, I’m not entirely convinced that a Gaussian process would actually be the best way to model timeseries data if you’re interested in making extrapolations. Gaussian processes are sort of an advanced form of interpolation where you try to find correlations among nearby data points. Depending on your covariance kernel, it’s often not going to be much more useful for extrapolation than regular interpolation.

@Sjoerd you correctly guessed I’m interested in extrapolating time series data! The GP has the appealing property of describing the uncertainty in its predictions with a distribution and it doesn’t require regularly sampled data, of course uncertainty can also be quantified in linear regression and autoregressive models too – I’m going to end up testing a dozen classes of model…