{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Dynamic Models\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "\n", "### Preliminaries\n", "\n", "- Goal \n", " - Introduction to dynamic (=temporal) Latent Variable Models, including the Hidden Markov Model and Kalman filter. \n", "- Materials\n", " - Mandatory\n", " - These lecture notes\n", " - Optional \n", " - Bishop pp.605-615 on Hidden Markov Models\n", " - Bishop pp.635-641 on Kalman filters\n", " - Faragher (2012), [Understanding the Basis of the Kalman Filter](https://github.com/bertdv/BMLIP/blob/master/lessons/notebooks/files/Faragher-2012-Understanding-the-Basis-of-the-Kalman-Filter.pdf)\n", " - Minka (1999), [From Hidden Markov Models to Linear Dynamical Systems](https://github.com/bertdv/BMLIP/blob/master/lessons/notebooks/files/Minka-1999-from-HMM-to-LDS.pdf)\n", " " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Example Problem\n", "\n", "- We consider a one-dimensional cart position tracking problem, see [Faragher 2012](https://github.com/bertdv/BMLIP/blob/master/lessons/notebooks/files/Faragher-2012-Understanding-the-Basis-of-the-Kalman-Filter.pdf). \n", "\n", "- The hidden states are the position $z_t$ and velocity $\\dot z_t$. We can apply an external acceleration/breaking force $u_t$. (Noisy) observations are represented by $x_t$. \n", "\n", "- The equations of motions are given by\n", "\n", "\\begin{align}\n", "\\begin{bmatrix} z_t \\\\ \\dot{z_t}\\end{bmatrix} &= \\begin{bmatrix} 1 & \\Delta t \\\\ 0 & 1\\end{bmatrix} \\begin{bmatrix} z_{t-1} \\\\ \\dot z_{t-1}\\end{bmatrix} + \\begin{bmatrix} (\\Delta t)^2/2 \\\\ \\Delta t\\end{bmatrix} u_t + \\mathcal{N}(0,\\Sigma_z) \\\\\n", "x_t &= \\begin{bmatrix} z_t \\\\ \\dot{z_t}\\end{bmatrix} + \\mathcal{N}(0,\\Sigma_x) \n", "\\end{align}\n", "\n", "- Task: Infer the position $z_t$ after 10 time steps. (Solution later in this lesson).\n", "\n", "

\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Dynamical Models\n", "\n", "- In this lesson, we consider models where the sequence order of observations matters. \n", "\n", "- Consider the _ordered_ observation sequence $x^T \\triangleq \\left(x_1,x_2,\\ldots,x_T\\right)$.\n", " - (For brevity, in this lesson we use the notation $x_t^T$ to denote $(x_t,x_{t+1},\\ldots,x_T)$ and drop the subscript if $t=1$, so $x^T = x_1^T = \\left(x_1,x_2,\\ldots,x_T\\right)$)." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- We wish to develop a generative model\n", " $$p( x^T )$$\n", "that 'explains' the time series $x^T$. " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- We cannot use the IID assumption $p( x^T ) = \\prod_t p(x_t )$. In general, we _can_ use the **chain rule** (a.k.a. **the general product rule**)\n", "\n", "\\begin{align*}\n", "p(x^T) &= p(x_T|x^{T-1}) \\,p(x^{T-1}) \\\\\n", " &= p(x_T|x^{T-1}) \\,p(x_{T-1}|x^{T-2}) \\cdots p(x_2|x_1)\\,p(x_1) \\\\\n", " &= p(x_1)\\prod_{t=2}^T p(x_t\\,|\\,x^{t-1})\n", "\\end{align*}" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- Generally, we will want to limit the depth of dependencies on previous observations. For example, a $K$th-order linear **Auto-Regressive** (AR) model that is given by\n", "\\begin{align*}\n", " p(x_t\\,|\\,x^{t-1}) = \\mathcal{N}\\left(x_t \\,\\middle|\\, \\sum_{k=1}^K a_k x_{t-k}\\,,\\sigma^2\\,\\right) \n", "\\end{align*}\n", "limits the dependencies to the past $K$ samples." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### State-space Models\n", "\n", "- A limitation of AR models is that they need a lot of parameters in order to create a flexible model. E.g., if $x_t$ is an $M$-dimensional discrete variable, then a $K$th-order AR model will have about $M^{K}$ parameters. " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- Similar to our work on Gaussian Mixture models, we can create a flexible dynamic system by introducing _latent_ (unobserved) variables $z^T \\triangleq \\left(z_1,z_2,\\dots,z_T\\right)$ (one $z_t$ for each observation $x_t$). In dynamic systems, $z_t$ are called _state variables_." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- A **state space model** is a particular latent variable dynamical model defined by\n", "\\begin{align*}\n", " p(x^T,z^T) &= \\underbrace{p(z_1)}_{\\text{initial state}} \\prod_{t=2}^T \\underbrace{p(z_t\\,|\\,z_{t-1})}_{\\text{state transitions}}\\,\\prod_{t=1}^T \\underbrace{p(x_t\\,|\\,z_t)}_{\\text{observations}}\n", "\\end{align*}\n", " - The condition $p(z_t\\,|\\,z^{t-1}) = p(z_t\\,|\\,z_{t-1})$ is called a $1$st-order Markov condition.\n", "\n", "" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- The Forney-style factor graph for a state-space model:\n", "\n", "

" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Hidden Markov Models and Linear Dynamical Systems\n", "\n", "- A **Hidden Markov Model** (HMM) is a specific state-space model with discrete-valued state variables $z_t$." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- Typically, $z_t$ is a $K$-dimensional one-hot coded latent 'class indicator' with transition probabilities $a_{jk} \\triangleq p(z_{tk}=1\\,|\\,z_{t-1,j}=1)$, or equivalently,\n", " $$p(z_t|z_{t-1}) = \\prod_{k=1}^K \\prod_{j=1}^K a_{jk}^{z_{t-1,j}\\cdot z_{tk}}$$\n", "which is usually accompanied by an initial state distribution $p(z_{1k}=1) = \\pi_k$." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ " \n", "- The classical HMM has also discrete-valued observations but in pratice any (probabilistic) observation model $p(x_t|z_t)$ may be coupled to the hidden Markov chain. \n", "\n", "" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- Another well-known state-space model with continuous-valued state variables $z_t$ is the **(Linear) Gaussian Dynamical System** (LGDS), which is defined as\n", "\n", "\\begin{align*}\n", "p(z_t\\,|\\,z_{t-1}) &= \\mathcal{N}\\left(\\, A z_{t-1}\\,,\\,\\Sigma_z\\,\\right) \\\\ \n", "p(x_t\\,|\\,z_t) &= \\mathcal{N}\\left(\\, C z_t\\,,\\,\\Sigma_x\\,\\right) \\\\\n", "p(z_1) &= \\mathcal{N}\\left(\\, \\mu_1\\,,\\,\\Sigma_1\\,\\right)\n", "\\end{align*}\n", "" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "- Note that the joint distribution over all states and observations $\\{(x_1,z_1),\\ldots,(x_t,z_t)\\}$ is a (large-dimensional) Gaussian distribution. This means that, in principle, every inference problem on the LGDS model also leads to a Gaussian distribution." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- HMM's and LGDS's (and variants thereof) are at the basis of a wide range of complex information processing systems, such as speech and language recognition, robotics and automatic car navigation, and even processing of DNA sequences. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Common Signal Processing Tasks as Message Passing-based Inference\n", "\n", "- As we have seen, inference tasks in linear Gaussian state space models can be analytically solved.\n", "\n", "- However, these derivations quickly become cumbersome and prone to errors.\n", "\n", "- Alternatively, we could specify the generative model in a (Forney-style) factor graph and use automated message passing to infer the posterior over the hidden variables. Here follows some examples.\n", "\n", "- **Filtering**, a.k.a. state estimation: estimation of a state (at time step $t$), based on past and current (at $t$) observations. \n", "

\n", "\n", "- **Smoothing**: estimation of a state based on both past and future observations. Needs backward messages from the future. \n", "\n", "

\n", "\n", "- **Prediction**: estimation of future state or observation based only on observations of the past.\n", "\n", "

\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Kalman Filtering\n", "\n", "- Technically, a [**Kalman filter**](https://en.wikipedia.org/wiki/Kalman_filter) is the solution to the recursive estimation (inference) of the hidden state $z_t$ based on past observations in an LGDS, i.e., Kalman filtering solves the problem $p(z_t\\,|\\,x^t)$ based on the previous estimate $p(z_{t-1}\\,|\\,x^{t-1})$ and a new observation $x_t$ (in the context of the given model specification of course). \n", " " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ " \n", "- Let's infer the Kalman filter for a scalar linear Gaussian dynamical system:\n", "\\begin{align*}\n", " p(z_t\\,|\\,z_{t-1}) &= \\mathcal{N}(z_t\\,|\\,a z_{t-1},\\sigma_z^2) \\tag{state transition} \\\\\n", " p(x_t\\,|\\,z_t) &= \\mathcal{N}(x_t\\,|\\,c z_t,\\sigma_x^2) \\tag{observation} \n", "\\end{align*}" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ " \n", "- Kalman filtering comprises inferring $p(z_t\\,|\\,x^t)$ from a given prior estimate $p(z_{t-1}\\,|\\,x^{t-1})$ (available after the previous time step) and a new observation $x_t$. Let us assume that \n", "\\begin{align} \n", "p(z_{t-1}\\,|\\,x^{t-1}) = \\mathcal{N}(z_{t-1} \\,|\\, \\mu_{t-1}, \\sigma_{t-1}^2) \\tag{prior}\n", "\\end{align} " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "- Note that everything is Gaussian, so it is _in principle_ possible to execute inference problems analytically and the result will be a Gaussian posterior:\n", " - (In the following derivation we make use of the renormalization equality $\\mathcal{N}(x\\,|\\,cz,\\sigma^2) = \\frac{1}{c}\\mathcal{N}\\left(z \\,\\big|\\,\\frac{x}{c},\\left(\\frac{\\sigma}{c}\\right)^2\\right)$).\n", "\n", "\\begin{align*}\n", "\\underbrace{p(z_t\\,|\\,x^t)}_{\\text{posterior}} &= p(z_t\\,|\\,x_t,x^{t-1}) \\propto p(x_t,z_t\\,|\\,x^{t-1}) \\\\\n", " &\\propto p(x_t\\,|\\,z_t) \\,p(z_t\\,|\\,x^{t-1}) \\\\\n", " &= p(x_t\\,|\\,z_t) \\, \\sum_{z_{t-1}} p(z_t,z_{t-1}\\,|\\,x^{t-1}) \\\\\n", " &= \\underbrace{p(x_t\\,|\\,z_t)}_{\\text{observation}} \\, \\sum_{z_{t-1}} \\underbrace{p(z_t\\,|\\,z_{t-1})}_{\\text{state transition}} \\, \\underbrace{p(z_{t-1}\\,|\\,x^{t-1})}_{\\text{prior}} \\\\\n", " &= \\mathcal{N}(x_t\\,|\\,c z_t,\\sigma_x^2) \\sum_{z_{t-1}} \\mathcal{N}(z_t\\,|\\,a z_{t-1},\\sigma_z^2) \\, \\mathcal{N}(z_{t-1} \\,|\\, \\mu_{t-1}, \\sigma_{t-1}^2) \\\\\n", " &= \\frac{1}{c}\\mathcal{N}\\left(z_t\\bigm| \\frac{x_t}{c} ,\\left(\\frac{\\sigma_x}{c}\\right)^2\\right) \\sum_{z_{t-1}} \\frac{1}{a}\\underbrace{\\mathcal{N}\\left(z_{t-1}\\bigm| \\frac{z_t}{a},\\left(\\frac{\\sigma_z}{a}\\right)^2 \\right) \\, \\mathcal{N}(z_{t-1} \\,|\\, \\mu_{t-1}, \\sigma_{t-1}^2)}_{\\text{use Gaussian multiplication formula SRG-6}} \\\\\n", " &\\propto \\underbrace{\\mathcal{N}\\left(z_t\\,\\bigm| \\,\\frac{x_t}{c} ,\\left(\\frac{\\sigma_x}{c}\\right)^2\\right) \\cdot \\mathcal{N}\\left(z_t\\, \\bigm|\\,a \\mu_{t-1},\\sigma_z^2 + \\left(a \\sigma_{t-1}\\right)^2 \\right)}_{\\text{use SRG-6 again}} \\\\\n", " &\\propto \\mathcal{N}\\left( z_t \\,|\\, \\mu_t, \\sigma_t^2\\right)\n", "\\end{align*}\n", "with\n", "\\begin{align*}\n", " \\rho_t^2 &= a^2 \\sigma_{t-1}^2 + \\sigma_z^2 \\tag{predicted variance}\\\\\n", " K_t &= \\frac{c \\rho_t^2}{c^2 \\rho_t^2 + \\sigma_x^2} \\tag{Kalman gain} \\\\\n", " \\mu_t &= \\underbrace{a \\mu_{t-1}}_{\\text{prior prediction}} + K_t \\cdot \\underbrace{\\left( x_t - c a \\mu_{t-1}\\right)}_{\\text{prediction error}} \\tag{posterior mean}\\\\\n", " \\sigma_t^2 &= \\left( 1 - c\\cdot K_t \\right) \\rho_t^2 \\tag{posterior variance}\n", "\\end{align*}" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "- Kalman filtering consists of computing/updating these last four equations for each new observation ($x_t$). This is a very efficient recursive algorithm to estimate the state $z_t$ from all observations (until $t$).\n", "\n", "- It turns out that it's also possible to get an analytical result for $p(x_t|x^{t-1})$, which is the **model evidence** in a filtering context. See [optional slides](#kalman-proof) for details. " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Multi-dimensional Kalman Filtering\n", "\n", "- The Kalman filter equations can also be derived for multidimensional state-space models. In particular, for the model\n", "\\begin{align*}\n", "z_t &= A z_{t-1} + \\mathcal{N}(0,\\Gamma) \\\\\n", "x_t &= C z_t + \\mathcal{N}(0,\\Sigma)\n", "\\end{align*}\n", "the Kalman filter update equations for the posterior $p(z_t |x^t) = \\mathcal{N}\\left(z_t \\bigm| \\mu_t, V_t \\right)$ are given by (see Bishop, pg.639)\n", "\\begin{align*}\n", "P_t &= A V_{t-1} A^T + \\Gamma \\tag{predicted variance}\\\\\n", "K_t &= P_t C^T \\cdot \\left(C P_t C^T + \\Sigma \\right)^{-1} \\tag{Kalman gain} \\\\\n", "\\mu_t &= A \\mu_{t-1} + K_t\\cdot\\left(x_t - C A \\mu_{t-1} \\right) \\tag{posterior state mean}\\\\\n", "V_t &= \\left(I-K_t C \\right) P_{t} \\tag{posterior state variance}\n", "\\end{align*}\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Code Example: Kalman Filtering and the Cart Position Tracking Example Revisited\n", "\n", "\n", "- We can now solve the cart tracking problem of the introductory example by implementing the Kalman filter." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "using Pkg;Pkg.activate(\"probprog/workspace/\");Pkg.instantiate()\n", "IJulia.clear_output();" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Prediction: 𝒩(m=[42.21, 4.51], v=[[1.30, 0.39][0.39, 0.34]])" ] }, { "data": { "image/png": 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"text/plain": [ "Figure(PyObject