Particle filter and unscented Kalman filter in a nutshell

Suppose you have a linear dynamic system. That is, the function that predicts the next state from the current state to the next is linear. Suppose also that the states in your system are not known precisely but have some uncertainty modeled by a (multivariate) normal distribution. Then the uncertainty in the state at the next step also has a normal distribution, because a linear transformation of a normal distribution remains normal. This is a very high-level description of the classic Kalman filter.

When the transition from one state to another is nonlinear, the probability distribution around future states is not normal. There are many variations on the Kalman filter that amount to various approximations to get around this core difficulty: extended Kalman filters, unscented Kalman filters, particle filters, etc. Here I’ll just discuss unscented Kalman filters and particle filters. This will be a very hand-wavy discussion, but it will give the basic ideas.

It’s easy to push discrete random points through a nonlinear transformation. Calculating the effect of a nonlinear transformation on continuous random variables is more work. The idea of an unscented Kalman filter is to create a normal distribution that approximates the result of a nonlinear transformation by seeing what happens to a few carefully chosen points. The idea of particle filtering is to randomly generate a cloud of points and push these points through the nonlinear transformation.

This may make it sound like (unscented) Kalman filters and particle filters are trivial to implement. Far from it. The basic framework is easy to understand, but getting good performance on a particular problem can take a lot of work.

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Kalman filters and functional programming

A few weeks ago I started a series of posts on various things you could do with a functional fold. In the first post I mentioned that the idea came from a paper by Brian Beckman on Kalman filters and folds:

This post was inspired by a paper by Brian Beckman (in progress) that shows how a Kalman filter can be implemented as a fold. From a Bayesian perspective, the thing that makes the Kalman filter work is that a certain multivariate normal model has a conjugate prior. This post shows that conjugate models more generally can be implemented as folds over the data. That’s interesting, but what does it buy you? Brian’s paper discusses this in detail, but one advantage is that it completely separates the accumulator function from the data, so the former can be tested in isolation.

At the time Brian was working on one big paper in private. This has since been split into several papers and they’re now public.

Functional folds and conjugate models Bayesian calculations are intrinsically recursive: The posterior distribution from one step becomes the prior for the next step.

With conjugate models, the prior and posterior belong to the same family of distributions. If a distribution from this family is determined by a fixed set of parameters, we only need to update these parameters at each step. This updating process is defined by an integration problem, i.e. applying Bayes’ theorem, but it’s common with conjugate models for the integration to reduce to a simple algebraic operation on the parameters.

For example, suppose some binary event that happens with probability θ where θ has a beta(α, β) distribution. When we take our next observation, the posterior distribution becomes beta(α+1, β) if the event occurred and beta(α, β+1) if it didn’t. There is an integration problem in the background that reduces to simply adding a 1 to the α parameter for every success and to the β parameter for every failure. (Standard terminology is to call an observation of the thing you’re interested in a “success” regardless of whether the thing you’re observing is good or bad. “Failure” is just an observation of the thing not happening, even if it’s good that it didn’t happen.)

Conjugate models have the structure of a fold in functional programming, also known as a reduce or accumulate operation. We’ll show below how to compute the posterior distribution in a beta-binomial and normal-normal model using folds in Haskell. (Technically a left associative fold, foldl. Haskell also has a right associative fold foldr that we won’t be concerned about here.)

What does foldl require? Let’s ask GHCi, the Haskell REPL. Prior to GHC version 7.10 we would get the following.

ghci> :type foldl
foldl :: (a -> b -> a) -> a -> [b] -> a

Unfortunately the type variables a and b just mean “one type” and “a possibly different type” and tell us nothing about how the types are used, so let’s unravel this a little at a time.

Here “a” is the type of the accumulator state. In our case, it will be the prior and posterior distribution parameters because that’s what’s being updated for each data point. Conjugacy makes this work since the prior and posterior distributions belong to the same family. The “b” type is our data. So you could read the type of foldl as follows.

foldl takes three things: a function to fold over the data, an initial accumulator state, and an array of data. It returns the final accumulator state. The accumulator function takes an accumulator state and a data value, and returns a new accumulator state.

Starting with GHC 7.10 the type of foldl is a little more general.

ghci> :type foldl
foldl :: Foldable t => (a -> b -> a) -> a -> t b -> a

Instead of foldl operating only on lists, it can operate on any “foldable” container. To recover the type from earlier versions of the compiler, replace t with []. (Haskell uses [] b and [b] as equivalent ways of indicating a list of elements of type b.) There is one other difference I’ve edited out: The latest version of GHCi reverses the roles of ‘a’ and ‘b’. This is confusing, but has no effect since the type variable names have no meaning. I swapped the a’s and b’s back like they were to make the two definitions easier to compare.

Beta-binomial example

Returning to the beta-binomial model discussed above, suppose we have a sequence of observations consisting of a 1 when the event in question happened and a 0 when it did not. And suppose our prior distribution on the probability of the event has a beta(α, β) distribution.

Now what function will we fold over our data to update our prior into a posterior?

ghci> let f params y = (fst params + y, snd params + 1 - y)

The function f takes a pair of numbers, the two beta distribution parameters, and a data point y, and returns an updated set of beta parameters. If the data point is a 1 (success) then the α> parameter is incremented, and if the data point is a 0 (failure) then the β parameter is incremented. If we start with a beta(0.2, 0.8) prior and observe the data [1, 1, 0, 0, 1] then we apply f to our data as follows.

ghci> let d = [1, 1, 0, 0, 1]
ghci> foldl f (0.2, 0.8) d

The result will be (3.2, 2.8). The three successes incremented the first beta parameter and the two failures incremented the second beta parameter.

To see how the foldl operates, we can run scanl instead. It works like foldl but returns a list of intermediate accumulator values rather than just the final accumulator value.

ghci> scanl f (0.2, 0.8) d

This returns

[ (0.2, 0.8), (1.2, 0.8), (2.2, 0.8), (2.2, 1.8), (2.2, 2.8), (3.2, 2.8) ].

he initial accumulator state is (0.2, 0.8) because we started with a beta(0.2, 0.8) prior. Then we increment the accumulator state to (1.2, 0.8) because the first data point was a 1. Then we increment the state to (2.2, 0.8) because the second data point is also a 1. Next we see two failures in a row and so the second part of the accumulator is incremented two times. After observing the last data point, a success, our final state is (3.2, 2.8), just as when we applied foldl.

Normal-normal example

Next we consider another Bayesian model with a conjugate prior. We assume our data come from a normal distribution with mean θ and known variance 1. We assume a conjugate prior distribution on θ, normal with mean μ0 and variance τ02.

This time the posterior distribution calculation is more complicated and so our accumulator function is more complicated. But the form of the calculation is the same as above: we fold an accumulator function over the data.

Here is the function that encapsulates our posterior distribution calculation.

f params y = ((m/v  + y)*newv, newv)
where
m = fst params -- mean
v = snd params -- variance
newv = v/(v + 1)

Now suppose our prior distribution on θ has mean 0 and variance 4, and we observe two values, [3, 5].

ghci> foldl f (0, 4) [3, 5]

This returns (3.5555, 0.4444). To see the intermediate accumulation state, i.e. after just seeing y = 3, we run scanl instead and see

[ (0, 4), (2,4, 0,8), (3.5555, 0.4444) ]

Inspiration

This post was inspired by a paper by Brian Beckman (in progress) that shows how a Kalman filter can be implemented as a fold. From a Bayesian perspective, the thing that makes the Kalman filter work is that a certain multivariate normal model has a conjugate prior. This post shows that conjugate models more generally can be implemented as folds over the data. That’s interesting, but what does it buy you? Brian’s paper discusses this in detail, but one advantage is that it completely separates the accumulator function from the data, so the former can be tested in isolation.

Next up: ODEs

The next post shows how to implement an ODE solver (4th order Runge-Kutta) as a fold over the list of time steps where the solution is needed. Kalman filters and bottom-up learning Kalman filtering is a mixture of differential equations and statistics. Kalman filters are commonly used in tracking applications, such as tracking the location of a space probe or tracking the amount of charge left in a cell phone battery. Kalman filters provide a way to synthesize theoretical predictions and actual measurements, accounting for error in both.

Engineers naturally emphasize the differential equations and statisticians naturally emphasize the statistics. Both perspectives are valuable, but in my opinion/experience, the engineering perspective should come first.

From an engineering perspective, a Kalman filtering problem starts as a differential equation. In an ideal world, one would simply solve the differential equation and be done. But the experienced engineer realizes his or her differential equations don’t capture everything. (Unlike the engineer in this post.) Along the road to the equations at hand, there were approximations, terms left out, and various unknown unknowns.

The Kalman filter accounts for some level of uncertainty in the process dynamics and in the measurements taken. This uncertainty is modeled as randomness, but this doesn’t mean that there’s necessarily anything “random” going on. It simply acknowledges that random variables are an effective way of modeling miscellaneous effects that are unknown or too complicated to account for directly. (See Random is as random does.)

The statistical approach to Kalman filtering is to say that it is simply another estimation problem. You start from a probability model and apply Bayes’ theorem. That probability model has a term inside that happens to come from a differential equation in practice, but this is irrelevant to the statistics. The basic Kalman filter is a linear model with normal probability distributions, and this makes a closed-form solution for the posterior possible.

You’d be hard pressed to start from a statistical description of Kalman filtering, such as that given here, and have much appreciation for the motivating dynamics. Vital details have simply been abstracted away. As a client told me once when I tried to understand his problem starting from the top-down, “You’ll never get here from there.”

The statistical perspective is complementary. Some things are clear from the beginning with the statistical formulation that would take a long time to see from the engineering perspective. But while both perspectives are valuable, I believe it’s easier to start on the engineering end and work toward the statistics end rather than the other way around.

History supports this claim. The Kalman filter from the engineering perspective came first and its formulation in terms of Bayesian statistics came later. Except that’s not entirely true.

Rudolf Kálmán published his seminal paper in 1960 and four years later papers started to come out making the connection to Bayesian statistics. But while Kálmán and others were working in the US starting from the engineering end, Ruslan Stratonovich was working in Russia starting from the statistical end. Still, I believe it’s fair to say that most of the development and application of Kalman filters has proceeded from the engineering to the statistics rather than the other way around.

RelatedMore on Kalman filters

Bring out your equations!

Nice discussion from Fundamentals of Kalman Filtering: A Practical Approach by Paul Zarchan and Howard Musoff:

Often the hardest part in Kalman filtering is the subject that no one talks about—setting up the problem. This is analogous to the quote from the recent engineering graduate who, upon arriving in industry, enthusiastically says, “Here I am, present me with your differential equations!” As the naive engineering graduate soon found out, problems in the real world are frequently not clear and are subject to many interpretations. Real problems are seldom presented in the form of differential equations, and they usually do not have unique solutions.

Whether it’s Kalman filters, dynamical systems, or anything else, setting up the problem is the hard part, or at least a hard part.

On the other hand, it’s about as impractical to only be able to set up problems as it is to only be able to solve them. You have to know what kinds of problems can be solved, and how accurately, so you can formulate a problem in a tractable way. There’s a feedback loop: provisional problem formulation, attempted solution, revised formulation, etc. It’s ideal when one person can set up and solve a problem, but it’s enough for the formulators and solvers to communicate well and have some common ground.

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Linear or not, random or not, at different levels

Linear vs nonlinear

I’ve run across a lot of ambiguity lately regarding systems described as “nonlinear.” Systems typically have several layers, and are linear at one level and nonlinear at another, and authors are not always clear about which level they’re talking about.

For example, I recently ran across something called a “nonlinear trapezoid filter.” My first instinct was to parse this as

nonlinear (trapezoid filter)

though I didn’t know what that meant. On closer inspection, I think they meant

(nonlinear trapezoid) filter

which is a linear filter, formed by multiplying a spectrum by a “nonlinear trapezoid,” a function whose graph looks like a trapezoid except one of the sides is slightly curved.

One of the things that prompted this post was a discussion with a client about Kalman filters and particle filters. You can have linear methods for tracking nonlinear processes, nonlinear methods for tracking nonlinear processes, etc. You have to be careful in this context when you call something “linear” or not.

Random vs deterministic

There’s a similar ambiguity around whether a system is random or deterministic. Systems are often deterministic at one level and random at another. For example, a bandit design is a deterministic rule for conducting an experiment with random outcomes. Simulations can be even more confusing because there could be several alternating layers of randomness and determinism. People can talk past each other while thinking they’re being clear. They can say something about variance, for example, and other other person nods their head, though they’re thinking about the variance of two different things.

As simple as it sounds, you can often help a team out by asking them to be more explicit when they say something is linear or nonlinear, random or deterministic. In a sense this is nothing new: it helps to be explicit. But I’m saying a little more than that, suggesting a couple particular areas to watch out for, areas where it’s common for people to be vague when they think they’re being clear.