{"product_id":"model-identification-and-data-analysis-hardback-9781119546368","title":"Model Identification and Data Analysis (Hardback) 9781119546368","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eModel Identification and Data Analysis\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cfont size=\"4\"\u003eSergio Bittanti (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781119546368, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 14 June 2019\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e416 pages\u003cbr\u003e23.1 x 15.5 x 2.5 cm, 0.771 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003eThis book is about constructing models from experimental data. It covers a range of topics, from statistical data prediction to Kalman filtering, from black-box model identification to parameter estimation, from spectral analysis to predictive control.\u003c\/p\u003e \u003cp\u003eWritten for graduate students, this textbook offers an approach that has proven successful throughout the many years during which its author has taught these topics at his University.\u003c\/p\u003e \u003cp\u003eThe book:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eContains accessible methods explained step-by-step in simple terms\u003c\/li\u003e \u003cli\u003eOffers an essential tool useful in a variety of fields, especially engineering, statistics, and mathematics\u003c\/li\u003e \u003cli\u003eIncludes an overview on random variables and stationary processes, as well as an introduction to discrete time models and matrix analysis\u003c\/li\u003e \u003cli\u003eIncorporates historical commentaries to put into perspective the developments that have brought the discipline to its current state\u003c\/li\u003e \u003cli\u003eProvides many examples and solved problems to complement the presentation and facilitate comprehension of the techniques presented\u003c\/li\u003e \u003c\/ul\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003eIntroduction xi\u003c\/p\u003e \u003cp\u003eAcknowledgments xv\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Stationary Processes and Time Series 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 1\u003c\/p\u003e \u003cp\u003e1.2 The Prediction Problem 1\u003c\/p\u003e \u003cp\u003e1.3 Random Variable 4\u003c\/p\u003e \u003cp\u003e1.4 Random Vector 5\u003c\/p\u003e \u003cp\u003e1.4.1 Covariance Coefficient 7\u003c\/p\u003e \u003cp\u003e1.5 Stationary Process 9\u003c\/p\u003e \u003cp\u003e1.6 White Process 11\u003c\/p\u003e \u003cp\u003e1.7 MA Process 12\u003c\/p\u003e \u003cp\u003e1.8 AR Process 16\u003c\/p\u003e \u003cp\u003e1.8.1 Study of the AR(1) Process 16\u003c\/p\u003e \u003cp\u003e1.9 Yule–Walker Equations 20\u003c\/p\u003e \u003cp\u003e1.9.1 Yule–Walker Equations for the AR(1) Process 20\u003c\/p\u003e \u003cp\u003e1.9.2 Yule–Walker Equations for the AR(2) and AR(n) Process 21\u003c\/p\u003e \u003cp\u003e1.10 ARMA Process 23\u003c\/p\u003e \u003cp\u003e1.11 Spectrum of a Stationary Process 24\u003c\/p\u003e \u003cp\u003e1.11.1 Spectrum Properties 24\u003c\/p\u003e \u003cp\u003e1.11.2 Spectral Diagram 25\u003c\/p\u003e \u003cp\u003e1.11.3 Maximum Frequency in Discrete Time 25\u003c\/p\u003e \u003cp\u003e1.11.4 White Noise Spectrum 25\u003c\/p\u003e \u003cp\u003e1.11.5 Complex Spectrum 26\u003c\/p\u003e \u003cp\u003e1.12 ARMA Model: Stability Test and Variance Computation 26\u003c\/p\u003e \u003cp\u003e1.12.1 Ruzicka Stability Criterion 28\u003c\/p\u003e \u003cp\u003e1.12.2 Variance of an ARMA Process 32\u003c\/p\u003e \u003cp\u003e1.13 FundamentalTheorem of Spectral Analysis 35\u003c\/p\u003e \u003cp\u003e1.14 Spectrum Drawing 38\u003c\/p\u003e \u003cp\u003e1.15 Proof of the FundamentalTheorem of Spectral Analysis 43\u003c\/p\u003e \u003cp\u003e1.16 Representations of a Stationary Process 45\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Estimation of Process Characteristics 47\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 47\u003c\/p\u003e \u003cp\u003e2.2 General Properties of the Covariance Function 47\u003c\/p\u003e \u003cp\u003e2.3 Covariance Function of ARMA Processes 49\u003c\/p\u003e \u003cp\u003e2.4 Estimation of the Mean 50\u003c\/p\u003e \u003cp\u003e2.5 Estimation of the Covariance Function 53\u003c\/p\u003e \u003cp\u003e2.6 Estimation of the Spectrum 55\u003c\/p\u003e \u003cp\u003e2.7 Whiteness Test 57\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Prediction 61\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 61\u003c\/p\u003e \u003cp\u003e3.2 Fake Predictor 62\u003c\/p\u003e \u003cp\u003e3.2.1 Practical Determination of the Fake Predictor 64\u003c\/p\u003e \u003cp\u003e3.3 Spectral Factorization 66\u003c\/p\u003e \u003cp\u003e3.4 Whitening Filter 70\u003c\/p\u003e \u003cp\u003e3.5 Optimal Predictor from Data 71\u003c\/p\u003e \u003cp\u003e3.6 Prediction of an ARMA Process 76\u003c\/p\u003e \u003cp\u003e3.7 ARMAX Process 77\u003c\/p\u003e \u003cp\u003e3.8 Prediction of an ARMAX Process 78\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Model Identification 81\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 81\u003c\/p\u003e \u003cp\u003e4.2 Setting the Identification Problem 82\u003c\/p\u003e \u003cp\u003e4.2.1 Learning from Maxwell 82\u003c\/p\u003e \u003cp\u003e4.2.2 A General Identification Problem 84\u003c\/p\u003e \u003cp\u003e4.3 Static Modeling 85\u003c\/p\u003e \u003cp\u003e4.3.1 Learning from Gauss 85\u003c\/p\u003e \u003cp\u003e4.3.2 Least Squares Made Simple 86\u003c\/p\u003e \u003cp\u003e4.3.2.1 Trend Search 86\u003c\/p\u003e \u003cp\u003e4.3.2.2 Seasonality Search 86\u003c\/p\u003e \u003cp\u003e4.3.2.3 Linear Regression 87\u003c\/p\u003e \u003cp\u003e4.3.3 Estimating the Expansion of the Universe 90\u003c\/p\u003e \u003cp\u003e4.4 Dynamic Modeling 92\u003c\/p\u003e \u003cp\u003e4.5 External RepresentationModels 92\u003c\/p\u003e \u003cp\u003e4.5.1 Box and Jenkins Model 92\u003c\/p\u003e \u003cp\u003e4.5.2 ARX and AR Models 93\u003c\/p\u003e \u003cp\u003e4.5.3 ARMAX and ARMA Models 94\u003c\/p\u003e \u003cp\u003e4.5.4 MultivariableModels 96\u003c\/p\u003e \u003cp\u003e4.6 Internal RepresentationModels 96\u003c\/p\u003e \u003cp\u003e4.7 The Model Identification Process 100\u003c\/p\u003e \u003cp\u003e4.8 The Predictive Approach 101\u003c\/p\u003e \u003cp\u003e4.9 Models in Predictive Form 102\u003c\/p\u003e \u003cp\u003e4.9.1 Box and Jenkins Model 103\u003c\/p\u003e \u003cp\u003e4.9.2 ARX and AR Models 103\u003c\/p\u003e \u003cp\u003e4.9.3 ARMAX and ARMA Models 104\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Identification of Input–Output Models 107\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 107\u003c\/p\u003e \u003cp\u003e5.2 Estimating AR and ARX Models: The Least Squares Method 107\u003c\/p\u003e \u003cp\u003e5.3 Identifiability 110\u003c\/p\u003e \u003cp\u003e5.3.1 The ̄R Matrix for the ARX(1, 1) Model 111\u003c\/p\u003e \u003cp\u003e5.3.2 The ̄R Matrix for a General ARX Model 112\u003c\/p\u003e \u003cp\u003e5.4 Estimating ARMA and ARMAX Models 115\u003c\/p\u003e \u003cp\u003e5.4.1 Computing the Gradient and the Hessian from Data 117\u003c\/p\u003e \u003cp\u003e5.5 Asymptotic Analysis 123\u003c\/p\u003e \u003cp\u003e5.5.1 Data Generation SystemWithin the Class of Models 125\u003c\/p\u003e \u003cp\u003e5.5.2 Data Generation System Outside the Class of Models 127\u003c\/p\u003e \u003cp\u003e5.5.2.1 Simulation Trial 132\u003c\/p\u003e \u003cp\u003e5.5.3 General Considerations on the Asymptotics of Predictive Identification 132\u003c\/p\u003e \u003cp\u003e5.5.4 Estimating the Uncertainty in Parameter Estimation 132\u003c\/p\u003e \u003cp\u003e5.5.4.1 Deduction of the Formula of the Estimation Covariance 134\u003c\/p\u003e \u003cp\u003e5.6 Recursive Identification 138\u003c\/p\u003e \u003cp\u003e5.6.1 Recursive Least Squares 138\u003c\/p\u003e \u003cp\u003e5.6.2 Recursive Maximum Likelihood 143\u003c\/p\u003e \u003cp\u003e5.6.3 Extended Least Squares 145\u003c\/p\u003e \u003cp\u003e5.7 Robustness of IdentificationMethods 147\u003c\/p\u003e \u003cp\u003e5.7.1 Prediction Error and Model Error 147\u003c\/p\u003e \u003cp\u003e5.7.2 Frequency Domain Interpretation 148\u003c\/p\u003e \u003cp\u003e5.7.3 Prefiltering 149\u003c\/p\u003e \u003cp\u003e5.8 Parameter Tracking 149\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Model Complexity Selection 155\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 155\u003c\/p\u003e \u003cp\u003e6.2 Cross-validation 157\u003c\/p\u003e \u003cp\u003e6.3 FPE Criterion 157\u003c\/p\u003e \u003cp\u003e6.3.1 FPE Concept 157\u003c\/p\u003e \u003cp\u003e6.3.2 FPE Determination 158\u003c\/p\u003e \u003cp\u003e6.4 AIC Criterion 160\u003c\/p\u003e \u003cp\u003e6.4.1 AIC Versus FPE 161\u003c\/p\u003e \u003cp\u003e6.5 MDL Criterion 161\u003c\/p\u003e \u003cp\u003e6.5.1 MDL Versus AIC 162\u003c\/p\u003e \u003cp\u003e6.6 Durbin–Levinson Algorithm 164\u003c\/p\u003e \u003cp\u003e6.6.1 Yule–Walker Equations for Autoregressive Models of Orders 1 and 2 165\u003c\/p\u003e \u003cp\u003e6.6.2 Durbin–Levinson Recursion: From AR(1) to AR(2) 166\u003c\/p\u003e \u003cp\u003e6.6.3 Durbin–Levinson Recursion for Models of Any Order 169\u003c\/p\u003e \u003cp\u003e6.6.4 Partial Covariance Function 171\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Identification of State Space Models 173\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 173\u003c\/p\u003e \u003cp\u003e7.2 Hankel Matrix 175\u003c\/p\u003e \u003cp\u003e7.3 Order Determination 176\u003c\/p\u003e \u003cp\u003e7.4 Determination of Matrices G and H 177\u003c\/p\u003e \u003cp\u003e7.5 Determination of Matrix F 178\u003c\/p\u003e \u003cp\u003e7.6 Mid Summary: An Ideal Procedure 179\u003c\/p\u003e \u003cp\u003e7.7 Order Determination with SVD 179\u003c\/p\u003e \u003cp\u003e7.8 Reliable Identification of a State Space Model 181\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Predictive Control 187\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 187\u003c\/p\u003e \u003cp\u003e8.2 Minimum Variance Control 188\u003c\/p\u003e \u003cp\u003e8.2.1 Determination of the MV Control Law 190\u003c\/p\u003e \u003cp\u003e8.2.2 Analysis of the MV Control System 192\u003c\/p\u003e \u003cp\u003e8.2.2.1 Structure 193\u003c\/p\u003e \u003cp\u003e8.2.2.2 Stability 193\u003c\/p\u003e \u003cp\u003e8.3 Generalized Minimum Variance Control 196\u003c\/p\u003e \u003cp\u003e8.3.1 Model Reference Control 198\u003c\/p\u003e \u003cp\u003e8.3.2 Penalized Control Design 200\u003c\/p\u003e \u003cp\u003e8.3.2.1 Choice A for Q(z) 201\u003c\/p\u003e \u003cp\u003e8.3.2.2 Choice B for Q(z) 203\u003c\/p\u003e \u003cp\u003e8.4 Model-Based Predictive Control 204\u003c\/p\u003e \u003cp\u003e8.5 Data-Driven Control Synthesis 205\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Kalman Filtering and Prediction 209\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 209\u003c\/p\u003e \u003cp\u003e9.2 Kalman Approach to Prediction and Filtering Problems 210\u003c\/p\u003e \u003cp\u003e9.3 The Bayes Estimation Problem 212\u003c\/p\u003e \u003cp\u003e9.3.1 Bayes Problem – Scalar Case 213\u003c\/p\u003e \u003cp\u003e9.3.2 Bayes Problem – Vector Case 215\u003c\/p\u003e \u003cp\u003e9.3.3 Recursive Bayes Formula – Scalar Case 215\u003c\/p\u003e \u003cp\u003e9.3.4 Innovation 217\u003c\/p\u003e \u003cp\u003e9.3.5 Recursive Bayes Formula – Vector Case 219\u003c\/p\u003e \u003cp\u003e9.3.6 Geometric Interpretation of Bayes Estimation 220\u003c\/p\u003e \u003cp\u003e9.3.6.1 Geometric Interpretation of the Bayes Batch Formula 220\u003c\/p\u003e \u003cp\u003e9.3.6.2 Geometric Interpretation of the Recursive Bayes Formula 222\u003c\/p\u003e \u003cp\u003e9.4 One-step-ahead Kalman Predictor 223\u003c\/p\u003e \u003cp\u003e9.4.1 The Innovation in the State Prediction Problem 224\u003c\/p\u003e \u003cp\u003e9.4.2 The State Prediction Error 224\u003c\/p\u003e \u003cp\u003e9.4.3 Optimal One-Step-Ahead Prediction of the Output 225\u003c\/p\u003e \u003cp\u003e9.4.4 Optimal One-Step-Ahead Prediction of the State 226\u003c\/p\u003e \u003cp\u003e9.4.5 Riccati Equation 228\u003c\/p\u003e \u003cp\u003e9.4.6 Initialization 231\u003c\/p\u003e \u003cp\u003e9.4.7 One-step-ahead Optimal Predictor Summary 232\u003c\/p\u003e \u003cp\u003e9.4.8 Generalizations 236\u003c\/p\u003e \u003cp\u003e9.4.8.1 System 236\u003c\/p\u003e \u003cp\u003e9.4.8.2 Predictor 236\u003c\/p\u003e \u003cp\u003e9.5 Multistep Optimal Predictor 237\u003c\/p\u003e \u003cp\u003e9.6 Optimal Filter 239\u003c\/p\u003e \u003cp\u003e9.7 Steady-State Predictor 240\u003c\/p\u003e \u003cp\u003e9.7.1 Gain Convergence 241\u003c\/p\u003e \u003cp\u003e9.7.2 Convergence of the Riccati Equation Solution 244\u003c\/p\u003e \u003cp\u003e9.7.2.1 Convergence Under Stability 244\u003c\/p\u003e \u003cp\u003e9.7.2.2 ConvergenceWithout Stability 246\u003c\/p\u003e \u003cp\u003e9.7.2.3 Observability 250\u003c\/p\u003e \u003cp\u003e9.7.2.4 Reachability 251\u003c\/p\u003e \u003cp\u003e9.7.2.5 General Convergence Result 256\u003c\/p\u003e \u003cp\u003e9.8 Innovation Representation 265\u003c\/p\u003e \u003cp\u003e9.9 Innovation Representation Versus Canonical Representation 266\u003c\/p\u003e \u003cp\u003e9.10 K-Theory Versus K–W Theory 267\u003c\/p\u003e \u003cp\u003e9.11 Extended Kalman Filter – EKF 271\u003c\/p\u003e \u003cp\u003e9.12 The Robust Approach to Filtering 273\u003c\/p\u003e \u003cp\u003e9.12.1 Norm of a Dynamic System 274\u003c\/p\u003e \u003cp\u003e9.12.2 Robust Filtering 276\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Parameter Identification in a Given Model 281\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 281\u003c\/p\u003e \u003cp\u003e10.2 Kalman Filter-Based Approaches 281\u003c\/p\u003e \u003cp\u003e10.3 Two-Stage Method 284\u003c\/p\u003e \u003cp\u003e10.3.1 First Stage – Data Generation and Compression 285\u003c\/p\u003e \u003cp\u003e10.3.2 Second Stage – Compressed Data Fitting 287\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Case Studies 291\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 291\u003c\/p\u003e \u003cp\u003e11.2 Kobe Earthquake Data Analysis 291\u003c\/p\u003e \u003cp\u003e11.2.1 Modeling the Normal Seismic Activity Data 294\u003c\/p\u003e \u003cp\u003e11.2.2 Model Validation 296\u003c\/p\u003e \u003cp\u003e11.2.3 Analysis of the Transition Phase via Detection Techniques 299\u003c\/p\u003e \u003cp\u003e11.2.4 Conclusions 300\u003c\/p\u003e \u003cp\u003e11.3 Estimation of a Sinusoid in Noise 300\u003c\/p\u003e \u003cp\u003e11.3.1 Frequency Estimation by Notch Filter Design 301\u003c\/p\u003e \u003cp\u003e11.3.2 Frequency Estimation with EKF 305\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix A Linear Dynamical Systems 309\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA.1 State Space and Input–Output Models 309\u003c\/p\u003e \u003cp\u003eA.1.1 Characteristic Polynomial and Eigenvalues 309\u003c\/p\u003e \u003cp\u003eA.1.2 Operator Representation 310\u003c\/p\u003e \u003cp\u003eA.1.3 Transfer Function 310\u003c\/p\u003e \u003cp\u003eA.1.4 Zeros, Poles, and Eigenvalues 310\u003c\/p\u003e \u003cp\u003eA.1.5 Relative Degree 311\u003c\/p\u003e \u003cp\u003eA.1.6 Equilibrium Point and System Gain 311\u003c\/p\u003e \u003cp\u003eA.2 Lagrange Formula 312\u003c\/p\u003e \u003cp\u003eA.3 Stability 312\u003c\/p\u003e \u003cp\u003eA.4 Impulse Response 313\u003c\/p\u003e \u003cp\u003eA.4.1 Impulse Response from a State Space Model 314\u003c\/p\u003e \u003cp\u003eA.4.2 Impulse Response from an Input–Output Model 314\u003c\/p\u003e \u003cp\u003eA.4.3 Quadratic Summability of the Impulse Response 315\u003c\/p\u003e \u003cp\u003eA.5 Frequency Response 315\u003c\/p\u003e \u003cp\u003eA.6 Multiplicity of State Space Models 316\u003c\/p\u003e \u003cp\u003eA.6.1 Change of Basis 316\u003c\/p\u003e \u003cp\u003eA.6.2 Redundancy in the System Order 317\u003c\/p\u003e \u003cp\u003eA.7 Reachability and Observability 318\u003c\/p\u003e \u003cp\u003eA.7.1 Reachability 318\u003c\/p\u003e \u003cp\u003eA.7.2 Observability 320\u003c\/p\u003e \u003cp\u003eA.7.3 PBH Test of Reachability and Observability 321\u003c\/p\u003e \u003cp\u003eA.8 System Decomposition 323\u003c\/p\u003e \u003cp\u003eA.8.1 Reachability and Observability Decompositions 323\u003c\/p\u003e \u003cp\u003eA.8.2 Canonical Decomposition 324\u003c\/p\u003e \u003cp\u003eA.9 Stabilizability and Detectability 328\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix B Matrices 331\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eB.1 Basics 331\u003c\/p\u003e \u003cp\u003eB.2 Eigenvalues 335\u003c\/p\u003e \u003cp\u003eB.3 Determinant and Inverse 337\u003c\/p\u003e \u003cp\u003eB.4 Rank 340\u003c\/p\u003e \u003cp\u003eB.5 Annihilating Polynomial 342\u003c\/p\u003e \u003cp\u003eB.6 Algebraic and Geometric Multiplicity 345\u003c\/p\u003e \u003cp\u003eB.7 Range and Null Space 345\u003c\/p\u003e \u003cp\u003eB.8 Quadratic Forms 346\u003c\/p\u003e \u003cp\u003eB.9 Derivative of a Scalar Function with Respect to a Vector 349\u003c\/p\u003e \u003cp\u003eB.10 Matrix Diagonalization via Similarity 350\u003c\/p\u003e \u003cp\u003eB.11 Matrix Diagonalization via Singular Value Decomposition 351\u003c\/p\u003e \u003cp\u003eB.12 Matrix Norm and Condition Number 353\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix C Problems and Solutions 357\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eBibliography 391\u003c\/p\u003e \u003cp\u003eIndex 397\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Electronics \u0026amp; communications engineering [\u003ca title=\"See our other books on Electronics \u0026amp; communications engineering\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Electronics%20\u0026amp;%20communications%20engineering%20%5BTJ%5D%22\"\u003eTJ\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley","offers":[{"title":"Brand New","offer_id":52428616532248,"sku":"9781119546368","price":90.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781119546368.jpg?v=1784681879","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/model-identification-and-data-analysis-hardback-9781119546368","provider":"Freshly Printed Books","version":"1.0","type":"link"}