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| 61 | <div class="navpath"><a class="el" href="tutorial.html">Tutorial in Bayesian estimation</a> |
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| [591] | 65 | |
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| 66 | |
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| 67 | <h1><a class="anchor" id="tut_arx">Theory of ARX model estimation </a></h1><p></p> |
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| 68 | <p>The <code>ARX</code> (AutoregRessive with eXogeneous input) model is defined as follows: </p> |
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| 69 | <p class="formulaDsp"> |
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| [608] | 70 | <img class="formulaDsp" alt="\[ y_t = \theta' \psi_t + \rho e_t \]" src="form_126.png"/> |
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| [591] | 71 | </p> |
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| [608] | 72 | <p> where <img class="formulaInl" alt="$y_t$" src="form_9.png"/> is the system output, <img class="formulaInl" alt="$[\theta,\rho]$" src="form_127.png"/> is vector of unknown parameters, <img class="formulaInl" alt="$\psi_t$" src="form_128.png"/> is an vector of data-dependent regressors, and noise <img class="formulaInl" alt="$e_t$" src="form_32.png"/> is assumed to be Normal distributed <img class="formulaInl" alt="$\mathcal{N}(0,1)$" src="form_129.png"/>.</p> |
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| [591] | 73 | <p>Special cases include: </p> |
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| 74 | <ul> |
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| [275] | 75 | <li>estimation of unknown mean and variance of a Gaussian density from independent samples.</li> |
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| 76 | </ul> |
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| [591] | 77 | <h2><a class="anchor" id="off"> |
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| [275] | 78 | Off-line estimation:</a></h2> |
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| [591] | 79 | <p>This particular model belongs to the exponential family, hence it has conjugate distribution (i.e. both prior and posterior) of the Gauss-inverse-Wishart form. See [ref]</p> |
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| 80 | <p>Estimation of this family can be achieved by accumulation of sufficient statistics. The sufficient statistics Gauss-inverse-Wishart density is composed of: </p> |
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| 81 | <dl> |
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| [275] | 82 | <dt>Information matrix </dt> |
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| 83 | <dd>which is a sum of outer products <p class="formulaDsp"> |
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| [608] | 84 | <img class="formulaDsp" alt="\[ V_t = \sum_{i=0}^{n} \left[\begin{array}{c}y_{t}\\ \psi_{t}\end{array}\right] \begin{array}{c} [y_{t}',\,\psi_{t}']\\ \\\end{array} \]" src="form_130.png"/> |
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| [591] | 85 | </p> |
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| [275] | 86 | </dd> |
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| 87 | <dt>"Degree of freedom" </dt> |
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| 88 | <dd>which is an accumulator of number of data records <p class="formulaDsp"> |
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| [608] | 89 | <img class="formulaDsp" alt="\[ \nu_t = \sum_{i=0}^{n} 1 \]" src="form_131.png"/> |
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| [591] | 90 | </p> |
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| [275] | 91 | </dd> |
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| 92 | </dl> |
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| [591] | 93 | <h2><a class="anchor" id="on"> |
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| [275] | 94 | On-line estimation</a></h2> |
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| [591] | 95 | <p>For online estimation with stationary parameters can be easily achieved by collecting the sufficient statistics described above recursively.</p> |
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| [608] | 96 | <p>Extension to non-stationaly parameters, <img class="formulaInl" alt="$ \theta_t , r_t $" src="form_132.png"/> can be achieved by operation called forgetting. This is an approximation of Bayesian filtering see [Kulhavy]. The resulting algorithm is defined by manipulation of sufficient statistics: </p> |
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| [591] | 97 | <dl> |
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| [275] | 98 | <dt>Information matrix </dt> |
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| 99 | <dd>which is a sum of outer products <p class="formulaDsp"> |
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| [608] | 100 | <img class="formulaDsp" alt="\[ V_t = \phi V_{t-1} + \left[\begin{array}{c}y_{t}\\ \psi_{t}\end{array}\right] \begin{array}{c} [y_{t}',\,\psi_{t}']\\ \\\end{array} +(1-\phi) V_0 \]" src="form_133.png"/> |
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| [591] | 101 | </p> |
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| [538] | 102 | </dd> |
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| [275] | 103 | <dt>"Degree of freedom" </dt> |
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| 104 | <dd>which is an accumulator of number of data records <p class="formulaDsp"> |
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| [608] | 105 | <img class="formulaDsp" alt="\[ \nu_t = \phi \nu_{t-1} + 1 + (1-\phi) \nu_0 \]" src="form_134.png"/> |
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| [591] | 106 | </p> |
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| [538] | 107 | </dd> |
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| [275] | 108 | </dl> |
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| [608] | 109 | <p>where <img class="formulaInl" alt="$ \phi $" src="form_135.png"/> is the forgetting factor, typically <img class="formulaInl" alt="$ \phi \in [0,1]$" src="form_136.png"/> roughly corresponding to the effective length of the exponential window by relation:</p> |
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| [591] | 110 | <p class="formulaDsp"> |
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| [608] | 111 | <img class="formulaDsp" alt="\[ \mathrm{win_length} = \frac{1}{1-\phi}\]" src="form_137.png"/> |
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| [591] | 112 | </p> |
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| [608] | 113 | <p> Hence, <img class="formulaInl" alt="$ \phi=0.9 $" src="form_138.png"/> corresponds to estimation on exponential window of effective length 10 samples.</p> |
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| 114 | <p>Statistics <img class="formulaInl" alt="$ V_0 , \nu_0 $" src="form_139.png"/> are called alternative statistics, their role is to stabilize estimation. It is easy to show that for zero data, the statistics <img class="formulaInl" alt="$ V_t , \nu_t $" src="form_140.png"/> converge to the alternative statistics.</p> |
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| [591] | 115 | <h2><a class="anchor" id="str"> |
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| [275] | 116 | Structure estimation</a></h2> |
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| [608] | 117 | <p>For this model, structure estimation is a form of model selection procedure. Specifically, we compare hypotheses that the data were generated by the full model with hypotheses that some regressors in vector <img class="formulaInl" alt="$\psi$" src="form_41.png"/> are redundant. The number of possible hypotheses is then the number of all possible combinations of all regressors.</p> |
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| 118 | <p>However, due to property known as nesting in exponential family, these hypotheses can be tested using only the posterior statistics. (This property does no hold for forgetting <img class="formulaInl" alt="$ \phi<1 $" src="form_141.png"/>). Hence, for low dimensional problems, this can be done by a tree search (method <a class="el" href="classbdm_1_1ARX.html#a16b02ae03316751664c22d59d90c1e34" title="Brute force structure estimation.">bdm::ARX::structure_est()</a>). Or more sophisticated algorithm [ref Ludvik]</p> |
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| [591] | 119 | <h2><a class="anchor" id="soft"> |
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| [275] | 120 | Software Image</a></h2> |
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| [591] | 121 | <p>Estimation of the ARX model is implemented in class <a class="el" href="classbdm_1_1ARX.html" title="Linear Autoregressive model with Gaussian noise.">bdm::ARX</a>. </p> |
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| 122 | <ul> |
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| [275] | 123 | <li>models from exponential family share some properties, these are encoded in class <a class="el" href="classbdm_1_1BMEF.html" title="Estimator for Exponential family.">bdm::BMEF</a> which is the parent of ARX </li> |
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| 124 | <li>one of the parameters of <a class="el" href="classbdm_1_1BMEF.html" title="Estimator for Exponential family.">bdm::BMEF</a> is the forgetting factor which is stored in attribute <code>frg</code>, </li> |
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| 125 | <li>posterior density is stored inside the estimator in the form of <a class="el" href="classbdm_1_1egiw.html" title="Gauss-inverse-Wishart density stored in LD form.">bdm::egiw</a> </li> |
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| 126 | <li>references to statistics of the internal <code>egiw</code> class, i.e. attributes <code>V</code> and <code>nu</code> are established for convenience.</li> |
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| 127 | </ul> |
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| [591] | 128 | <h2><a class="anchor" id="try"> |
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| [275] | 129 | How to try</a></h2> |
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| [608] | 130 | <p>The best way to experiment with this object is to run matlab script <code>arx_test.m</code> located in directory <code></code>./library/tutorial. See <a class="el" href="arx_ui.html">Running experiment <code>estimator</code> with ARX data fields this page is out of date, as the user info concept has been changed</a> for detailed description.</p> |
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| [275] | 131 | <ul> |
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| 132 | <li>In default setup, the parameters converge to the true values as expected. </li> |
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| 133 | <li>Try changing the forgetting factor, field <code>estimator.frg</code>, to values <1. You should see increased lower and upper bounds on the estimates. </li> |
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| 134 | <li>Try different set of parameters, filed <code>system.theta</code>, you should note that poles close to zero are harder to identify. </li> |
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| 135 | </ul> |
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| 136 | </div> |
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| [632] | 137 | <hr size="1"/><address style="text-align: right;"><small>Generated on Fri Sep 18 00:12:03 2009 for mixpp by |
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| [591] | 139 | <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.6.1 </small></address> |
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