{
 "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",
    "<p style=\"text-align:center;\"><img src=\"./figures/Faragher-2012-cart-1.png\" width=\"600px\"><p style=\"text-align:center;\">\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",
    "<!--- - In a Markovian state-space model, the observation sequence $x^T$ is not a first-order Markov chain, i.e., for the state-space model\n",
    "    $$\\begin{align*}\n",
    " p(x^T,z^T) &= p(z_1) \\prod_{t=2}^T p(z_t\\,|\\,z_{t-1})\\,\\prod_{t=1}^T p(x_t\\,|\\,z_t)\n",
    "\\end{align*}$$\n",
    "the following statement holds: \n",
    "    $$p(x_t\\,|\\,x_{t-1},x_{t-2}) \\neq p(x_t\\,|\\,x_{t-1})\\,.$$\n",
    "In other words, the latent variables $z_t$ represent a memory bank for past observations beyond $t-1$. (Proof as exercise). \n",
    "--->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "- The Forney-style factor graph for a state-space model:\n",
    "\n",
    "<p style=\"text-align:center;\"><img src=\"./figures/ffg-state-space.png\" width=\"600px\"></p>"
   ]
  },
  {
   "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 <span class=\"emphasis\">discrete-valued</span> 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",
    "<!---\n",
    "- The following figure shows the typical trellis structure of the many possible state transitions paths.   \n",
    "<img src=\"./figures/Figure13.7.png\" width=\"400px\">\n",
    "--->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "- Another well-known state-space model with <span class=\"emphasis\">continuous-valued</span> 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",
    "<!---or, equivalently (in the usual state-space notation)\n",
    "$$\\begin{align*}\n",
    "z_k &= A z_{k-1} + \\mathcal{N}\\left(0,\\Sigma_z \\right) \\\\ \n",
    "x_k &= C z_k + \\mathcal{N}\\left( 0, \\Sigma_x \\right) \\\\\n",
    "z_1 &= \\mu_1 + \\mathcal{N}\\left( 0, \\Sigma_1\\right)\n",
    "\\end{align*}$$\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",
    "<p style=\"text-align:center;\"><img src=\"./figures/ffg-state-space-filtering.png\" width=\"600px\"></p>\n",
    "\n",
    "- **Smoothing**: estimation of a state based on both past and future observations. Needs backward messages from the future.  \n",
    "\n",
    "<p style=\"text-align:center;\"><img src=\"./figures/ffg-state-space-smoothing.png\" width=\"600px\"></p>\n",
    "\n",
    "- **Prediction**: estimation of future state or observation based only on observations of the past.\n",
    "\n",
    "<p style=\"text-align:center;\"><img src=\"./figures/ffg-state-space-prediction.png\" width=\"600px\"></p>\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=[39.34, 3.83], v=[[1.30, 0.39][0.39, 0.34]])\n",
      "\n",
      "Measurement: 𝒩(m=[40.52, 2.10], v=[[1.00, 0.00][0.00, 2.00]])\n",
      "\n",
      "Posterior: 𝒩(m=[39.86, 3.80], v=[[0.55, 0.15][0.15, 0.24]])\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: Precompiling ForneyLab [9fc3f58a-c2cc-5bff-9419-6a294fefdca9]\n",
      "└ @ Base loading.jl:1423\n",
      "┌ Warning: `vendor()` is deprecated, use `BLAS.get_config()` and inspect the output instead\n",
      "│   caller = npyinitialize() at numpy.jl:67\n",
      "└ @ PyCall /Users/pol/.julia/packages/PyCall/3fwVL/src/numpy.jl:67\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Figure(PyObject <Figure size 1500x500 with 1 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "using LinearAlgebra, PyPlot\n",
    "include(\"scripts/cart_tracking_helpers.jl\")\n",
    "\n",
    "# Specify the model parameters\n",
    "Δt = 1.0                     # assume the time steps to be equal in size\n",
    "A = [1.0 Δt;\n",
    "     0.0 1.0]\n",
    "b = [0.5*Δt^2; Δt] \n",
    "Σz = convert(Matrix,Diagonal([0.2*Δt; 0.1*Δt])) # process noise covariance\n",
    "Σx = convert(Matrix,Diagonal([1.0; 2.0]))     # observation noise covariance;\n",
    "\n",
    "# Generate noisy observations\n",
    "n = 10                # perform 10 timesteps\n",
    "z_start = [10.0; 2.0] # initial state\n",
    "u = 0.2 * ones(n)     # constant input u\n",
    "noisy_x = generateNoisyMeasurements(z_start, u, A, b, Σz, Σx);\n",
    "\n",
    "m_z = noisy_x[1]                                    # initial predictive mean\n",
    "V_z = A * (1e8*Diagonal(I,2) * A') + Σz             # initial predictive covariance\n",
    "\n",
    "for t = 2:n\n",
    "    global m_z, V_z, m_pred_z, V_pred_z\n",
    "    #predict\n",
    "    m_pred_z = A * m_z + b * u[t]                   # predictive mean\n",
    "    V_pred_z = A * V_z * A' + Σz                    # predictive covariance\n",
    "    #update\n",
    "    gain = V_pred_z * inv(V_pred_z + Σx)            # Kalman gain\n",
    "    m_z = m_pred_z + gain * (noisy_x[t] - m_pred_z) # posterior mean update\n",
    "    V_z = (Diagonal(I,2)-gain)*V_pred_z             # posterior covariance update\n",
    "end\n",
    "println(\"Prediction: \",ProbabilityDistribution(Multivariate,GaussianMeanVariance,m=m_pred_z,v=V_pred_z))\n",
    "println(\"Measurement: \", ProbabilityDistribution(Multivariate,GaussianMeanVariance,m=noisy_x[n],v=Σx))\n",
    "println(\"Posterior: \", ProbabilityDistribution(Multivariate,GaussianMeanVariance,m=m_z,v=V_z))\n",
    "plotCartPrediction2(m_pred_z[1], V_pred_z[1], m_z[1], V_z[1], noisy_x[n][1], Σx[1][1]);"
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    "### The Cart Tracking Problem Revisited: Inference by Message Passing\n",
    "\n",
    "- Let's solve the cart tracking problem by sum-product message passing in a factor graph like the one depicted above. All we have to do is create factor nodes for the state-transition model $p(z_t|z_{t-1})$ and the observation model $p(x_t|z_t)$. Then we just build the factor graph and let [ForneyLab](http://forneylab.org) execute the message passing schedule. "
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    "- Since the factor graph is just a concatination of $n$ identical \"sections\" (one for each time step), we only have to specify a single section. When running the message passing algorithm we will explictly use the posterior of the previous timestep as prior in the next one. Let's build a section of the factor graph:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
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    "fg = FactorGraph()\n",
    "z_prev_m = Variable(id=:z_prev_m); placeholder(z_prev_m, :z_prev_m, dims=(2,))\n",
    "z_prev_v = Variable(id=:z_prev_v); placeholder(z_prev_v, :z_prev_v, dims=(2,2))\n",
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    "\n",
    "@RV z_prev ~ GaussianMeanVariance(z_prev_m, z_prev_v, id=:z_prev) # p(z_prev)\n",
    "@RV noise_z ~ GaussianMeanVariance(constant(zeros(2), id=:noise_z_m), constant(Σz, id=:noise_z_v)) # process noise\n",
    "@RV z = constant(A, id=:A) * z_prev + bu + noise_z; z.id = :z # p(z|z_prev) (state transition model)\n",
    "@RV x ~ GaussianMeanVariance(z, constant(Σx, id=:Σx)) # p(x|z) (observation model)\n",
    "placeholder(x, :x, dims=(2,));\n",
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    "Now that we've built the factor graph, we can perform Kalman filtering by inserting measurement data into the factor graph and performing message passing."
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     "text": [
      "Prediction: 𝒩(m=[39.34, 3.83], v=[[1.30, 0.39][0.39, 0.34]])\n",
      "\n",
      "Measurement: 𝒩(m=[40.52, 2.10], v=[[1.00, 0.00][0.00, 2.00]])\n",
      "\n",
      "Posterior: 𝒩(m=[39.86, 3.80], v=[[0.55, 0.15][0.15, 0.24]])\n",
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",
      "text/plain": [
       "Figure(PyObject <Figure size 1500x500 with 1 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "include(\"scripts/cart_tracking_helpers.jl\")\n",
    "algo = messagePassingAlgorithm(z)\n",
    "source_code = algorithmSourceCode(algo)\n",
    "eval(Meta.parse(source_code))\n",
    "marginals = Dict()\n",
    "messages = Array{Message}(undef,6)\n",
    "z_prev_m_0 = noisy_x[1]  \n",
    "z_prev_v_0 = A * (1e8*Diagonal(I,2) * A') + Σz \n",
    "for t=2:n\n",
    "    data = Dict(:x => noisy_x[t], :bu => b*u[t],:z_prev_m => z_prev_m_0, :z_prev_v => z_prev_v_0)\n",
    "    step!(data, marginals, messages) # perform msg passing (single timestep)\n",
    "    # Posterior of z becomes prior of z in the next timestep:\n",
    "    z_prev_m_0 = ForneyLab.unsafeMean(marginals[:z])\n",
    "    z_prev_v_0 = ForneyLab.unsafeCov(marginals[:z])\n",
    "end\n",
    "# Collect prediction p(z[n]|z[n-1]), measurement p(z[n]|x[n]), corrected prediction p(z[n]|z[n-1],x[n])\n",
    "prediction      = messages[5].dist # the message index is found by manual inspection of the schedule\n",
    "measurement     = messages[6].dist\n",
    "corr_prediction = convert(ProbabilityDistribution{Multivariate, GaussianMeanVariance}, marginals[:z])\n",
    "println(\"Prediction: \",prediction)\n",
    "println(\"Measurement: \",measurement)\n",
    "println(\"Posterior: \", corr_prediction)\n",
    "\n",
    "# Make a fancy plot of the prediction, noisy measurement, and corrected prediction after n timesteps\n",
    "plotCartPrediction(prediction, measurement, corr_prediction);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- Note that both the analytical Kalman filtering solution and the message passing solution lead to the same results. The advantage of message passing-based inference with ForneyLab is that we did not need to derive any inference equations. ForneyLab took care of all that. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Recap Dynamical Models \n",
    "\n",
    "- Dynamical systems do not obey the sample-by-sample independence assumption, but still can be specified, and state and parameter estimation equations can be solved by similar tools as for static models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- Two of the more famous and powerful models with latent states include the hidden Markov model (with discrete states) and the Linear Gaussian dynamical system (with continuous states)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- For the LGDS, the Kalman filter is a well-known recursive state estimation procedure. The Kalman filter can be derived through Bayesian update rules on Gaussian distributions. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "- If anything changes in the model, e.g., the state noise is not Gaussian, then you have to re-derive the inference equations again from scratch and it may not lead to an analytically pleasing answer. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "- $\\Rightarrow$ Generally, we will want to automate the inference processes. As we discussed, message passing in a factor graph is a visually appealing method to automate inference processes. We showed how Kalman filtering emerged naturally by automated message passing. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## <center> OPTIONAL SLIDES</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### <a id='kalman-proof'>Proof of Kalman filtering equations including evidence updating</a>\n",
    "\n",
    "- Now let's proof the Kalman filtering equations including evidence updating by probabilistic calculus:\n",
    "$$\\begin{align*}\n",
    "\\overbrace{p(z_t\\,|\\,x^t)}^{\\text{posterior}} &= p(z_t\\,|\\,x_t,x^{t-1}) \\\\\n",
    " &= \\frac{p(x_t,z_t\\,|\\,x^{t-1})}{p(x_t\\,|\\,x^{t-1})} \\\\\n",
    " &= \\frac{p(x_t\\,|\\,z_t) \\,p(z_t\\,|\\,x^{t-1})}{p(x_t\\,|\\,x^{t-1})} \\\\\n",
    " &= \\frac{p(x_t\\,|\\,z_t) \\, \\sum_{z_{t-1}} p(z_t,z_{t-1}\\,|\\,x^{t-1}) }{p(x_t\\,|\\,x^{t-1})} \\\\\n",
    " &= \\frac{p(x_t\\,|\\,z_t) \\, \\sum_{z_{t-1}} p(z_t\\,|\\,z_{t-1}) \\, p(z_{t-1}\\,|\\,x^{t-1})}{p(x_t\\,|\\,x^{t-1})} \\\\\n",
    " &= \\frac{\\overbrace{\\mathcal{N}(x_t|c z_t, \\sigma_x^2)}^{\\text{likelihood}} \\, \\sum_{z_{t-1}} \\overbrace{\\mathcal{N}(z_t\\,|\\,a z_{t-1},\\sigma_z^2)}^{\\text{state transition}} \\, \\overbrace{\\mathcal{N}(z_{t-1} \\,|\\, \\mu_{t-1}, \\sigma_{t-1}^2) }^{\\text{prior}} }{\\underbrace{p(x_t\\,|\\,x^{t-1})}_{\\text{evidence}}}\n",
    "\\end{align*}$$\n",
    " \n",
    "- The posterior  $p(z_t\\,|\\,x^t)$ is proportional to the numerator, which by making use of the renormalization equality \n",
    "$$\\begin{align}\n",
    "\\mathcal{N}(x\\,|\\,cz,\\sigma^2) = \\frac{1}{c}\\cdot \\mathcal{N}\\left(z \\,\\big|\\,\\frac{x}{c},\\left(\\frac{\\sigma}{c}\\right)^2\\right) \\tag{renormalization}\\,,\n",
    "\\end{align}$$\n",
    "can be evaluated with Gaussian multiplication rules:\n",
    "\n",
    "$$\\begin{align*} \n",
    "\\mathcal{N}(x_t|c z_t, &\\sigma_x^2) \\, \\sum_{z_{t-1}} \\underbrace{\\mathcal{N}(z_t\\,|\\,a z_{t-1},\\sigma_z^2)}_{\\text{use renormalization}} \\mathcal{N}(z_{t-1} \\,|\\, \\mu_{t-1}, \\sigma_{t-1}^2) \\\\\n",
    "&= \\mathcal{N}(x_t|c z_t, \\sigma_x^2) \\, \\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",
    "&= \\frac{1}{a} \\mathcal{N}(x_t|c z_t, \\sigma_x^2) \\, \\sum_{z_{t-1}} \\underbrace{\\mathcal{N}\\left(\\mu_{t-1}\\bigm|  \\frac{z_t}{a},\\left(\\frac{\\sigma_z}{a}\\right)^2 + \\sigma_{t-1}^2 \\right)}_{\\text{not a function of }z_{t-1}} \\underbrace{\\mathcal{N}(z_{t-1} \\,|\\, \\cdot, \\cdot)}_{\\text{sums to }1} \\\\\n",
    "&= \\frac{1}{a} \\underbrace{\\mathcal{N}(x_t|c z_t, \\sigma_x^2)}_{\\text{use renormalization rule}} \\, \\underbrace{\\mathcal{N}\\left(\\mu_{t-1}\\bigm|  \\frac{z_t}{a},\\left(\\frac{\\sigma_z}{a}\\right)^2 + \\sigma_{t-1}^2 \\right)}_{\\text{use renormalization rule}} \\\\\n",
    "&= \\frac{1}{c} \\underbrace{\\mathcal{N}\\left(z_t \\bigm|  \\frac{x_t}{c}, \\left( \\frac{\\sigma_x}{c}\\right)^2 \\right) \\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",
    "&= \\underbrace{\\frac{1}{c} \\mathcal{N}\\left( \\frac{x_t}{c} \\bigm| a \\mu_{t-1}, \\left( \\frac{\\sigma_x}{c}\\right)^2+ \\sigma_z^2 + \\left(a \\sigma_{t-1}\\right)^2\\right)}_{\\text{use renormalization}} \\, \\mathcal{N}\\left( z_t \\,|\\, \\mu_t, \\sigma_t^2\\right)\\\\\n",
    "  &= \\underbrace{\\mathcal{N}\\left(x_t \\,|\\, ca \\mu_{t-1}, \\sigma_x^2 + c^2(\\sigma_z^2+a^2\\sigma_{t-1}^2) \\right)}_{\\text{evidence } p(x_t|x^{t-1})} \\underbrace{\\mathcal{N}\\left( z_t \\,|\\, \\mu_t, \\sigma_t^2\\right)}_{\\text{posterior }p(z_t|x^t) }\n",
    "\\end{align*}$$\n",
    "with\n",
    "$$\\begin{align*}\n",
    "  \\rho_t^2 &= a^2 \\sigma_{t-1}^2 + \\sigma_z^2 \\tag{predicted variance}\\\\\n",
    "  K_t &= \\frac{c \\rho_t^2}{c^2 \\rho_t^2 + \\sigma_x^2} \\tag{Kalman gain} \\\\\n",
    "  \\mu_t &= \\underbrace{a \\mu_{t-1}}_{\\text{prior prediction}} + K_t \\cdot \\underbrace{\\left( x_t - c a \\mu_{t-1}\\right)}_{\\text{prediction error}} \\tag{posterior mean}\\\\\n",
    "  \\sigma_t^2 &= \\left( 1 - c\\cdot K_t \\right) \\rho_t^2 \\tag{posterior variance}\n",
    "\\end{align*}$$\n"
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    "### Extensions of Generative Gaussian Models\n",
    "\n",
    "- Using the methods of the previous lessons, it is possible to create your own new models based on stacking Gaussian and categorical distributions in new ways: \n",
    "\n",
    "<img src=\"./figures/fig-generative-Gaussian-models.png\" width=\"600px\">"
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