| Introduction | p. 1 |
| Chapter Parade | p. 2 |
| Part I: Fundamentals | p. 2 |
| Part II: Applications | p. 3 |
| Part III: Beyond the Poisson Point Process | p. 5 |
| Appendices | p. 5 |
| The Real Line Is Not Enough | p. 6 |
| General Point Processes | p. 6 |
| An Alternative Tradition | p. 8 |
| Fundamentals | |
| The Poisson Point Process | p. 11 |
| The Event Space | p. 12 |
| Intensity | p. 12 |
| Realizations | p. 13 |
| Likelihood Function | p. 17 |
| Expectations | p. 18 |
| Definition | p. 19 |
| Random Sums | p. 21 |
| Campbell's Theorem2 | p. 23 |
| Characterization of PPPs | p. 25 |
| Probability Generating Functional | p. 27 |
| Superposition | p. 28 |
| Independent (Bernoulli) Thinning | p. 30 |
| Declarations of Independence | p. 33 |
| Independent Scattering | p. 33 |
| Poisson's Gambit | p. 36 |
| Inevitability of the Poisson Distribution | p. 38 |
| Connection to Stochastic Processes | p. 41 |
| Nonlinear Transformations | p. 42 |
| Stochastic Transformations | p. 46 |
| Transition Processes | p. 46 |
| Measurement Processes | p. 47 |
| PPPs on Other Spaces | p. 50 |
| Discrete Spaces | p. 50 |
| Discrete-Continuous Spaces | p. 53 |
| Intensity Estimation | p. 57 |
| Maximum Likelihood Algorithms | p. 58 |
| Necessary Conditions | p. 59 |
| Gaussian Crosshairs and Edge Effects | p. 60 |
| Superposed Intensities with Sample Data | p. 63 |
| EM Method with Sample Data | p. 64 |
| Interpreting the Weights | p. 67 |
| Simple Examples | p. 67 |
| Affine Gaussian Sums | p. 69 |
| Superposed Intensities with Histogram Data | p. 73 |
| EM Method with Histogram Data | p. 73 |
| Affine Gaussian Sums | p. 76 |
| Regularization | p. 78 |
| Parametric Tying | p. 78 |
| Bayesian Methods | p. 80 |
| Cramér-Rao Bound (CRB) for Intensity Estimates | p. 81 |
| Background | p. 82 |
| Unbiased Estimators | p. 83 |
| Fisher Information Matrix and the Score Vector | p. 83 |
| CRB and the Cauchy-Schwarz Inequality | p. 84 |
| Spinoffs | p. 86 |
| CRB for PPP Intensity with Sample Data | p. 88 |
| CRB for PPP Intensity with Histogram Data | p. 90 |
| CRB for PPP Intensity on Discrete Spaces | p. 93 |
| Gating: Gauss on a Pedestal | p. 95 |
| Joint CRB for Gaussian Sums | p. 97 |
| Mean Vectors in a Gaussian Sum | p. 98 |
| Means and Coefficients in a Gaussian Sum | p. 99 |
| Observed Information Matrices | p. 100 |
| General Sums | p. 101 |
| Affine Gaussian Sums | p. 103 |
| Applications to Imaging, Tracking, and Distributed Sensing | |
| Tomographic Imaging | p. 109 |
| Positron Emission Tomography | p. 110 |
| PET: Time-of-Flight Data | p. 112 |
| Image Reconstruction | p. 113 |
| Small Cell Limit | p. 117 |
| Intuitive Interpretation | p. 118 |
| PET: Histogram Data | p. 118 |
| Detectors as a Discrete Space | p. 119 |
| Shepp-Vardi Algorithm | p. 119 |
| Single-Photon Computed Emission Tomography (SPECT) | p. 124 |
| Gamma Cameras | p. 124 |
| Image Reconstruction | p. 126 |
| Transmission Tomography | p. 134 |
| Background | p. 134 |
| Lange-Carson Algorithm | p. 135 |
| CRBs for Emission and Transmission Tomography | p. 142 |
| Regularization | p. 143 |
| Grenander's Method of Sieves | p. 143 |
| Multiple Target Tracking | p. 147 |
| Intensity Filters | p. 149 |
| PPP Model Interpretation | p. 149 |
| Predicted Target and Measurement Processes | p. 150 |
| Information Updates | p. 153 |
| The Final Filter | p. 156 |
| Relationship to Other Filters | p. 159 |
| Probability Hypothesis Density (PHD) Filter | p. 159 |
| Marked Multisensor Intensity Filter (MMIF) | p. 160 |
| Implementation | p. 161 |
| Particle Methods | p. 161 |
| Mean Shift Algorithm | p. 163 |
| Multimode Algorithms | p. 165 |
| Covariance Matrices | p. 166 |
| Gaussian Sum Methods | p. 167 |
| Regularization | p. 168 |
| Estimated Target Count | p. 171 |
| Sources of Error | p. 171 |
| Variance Reduction | p. 171 |
| Multiple Sensor Intensity Filters | p. 172 |
| Identical Coverage Sensors | p. 173 |
| Heterogeneous Sensor Coverages | p. 176 |
| Historical Note | p. 178 |
| Distributed Sensing | p. 179 |
| Distance Distributions | p. 180 |
| From Sensors To Target | p. 181 |
| Between Sensors | p. 185 |
| Communication Diversity | p. 189 |
| Detection Coverage | p. 190 |
| Stationary Sensor Fields | p. 191 |
| Drifting Fields and Anisotropy | p. 195 |
| Stereology | p. 198 |
| Beyond the Poisson Point Process | |
| A Profusion of Point Processes | p. 203 |
| Marked Processes | p. 204 |
| Product Space and Marking Theorem | p. 205 |
| Filtered Processes | p. 207 |
| FIM for Unbiased Estimators | p. 207 |
| Hard Core Processes | p. 208 |
| Cluster Processes | p. 210 |
| Poisson Cluster Processes | p. 210 |
| Neyman-Scott Processes | p. 211 |
| Cox (Doubly Stochastic) Processes | p. 213 |
| Equivalent Neyman-Scott Process | p. 214 |
| Intensity Function as Solution of an SDE | p. 215 |
| Markov Modulated Poisson Processes | p. 216 |
| Gibbs Point Processes | p. 216 |
| The Cutting Room Floor | p. 219 |
| Further Topics | p. 219 |
| Possible Trends | p. 221 |
| Expectation-Maximization (EM) Method | p. 223 |
| Formulation | p. 223 |
| E-step | p. 224 |
| M-step | p. 225 |
| Convergence | p. 225 |
| Iterative Majorization | p. 227 |
| Observed Information | p. 228 |
| Solving Conditional Mean Equations | p. 229 |
| Bayesian Filtering | p. 233 |
| General Recursion | p. 233 |
| Special Case: Kalman Filtering | p. 235 |
| Multitarget Tracking | p. 237 |
| Bayesian Derivation of Intensity Filters | p. 239 |
| Posterior Point Process | p. 239 |
| PPP Approximation | p. 241 |
| Altogether Now | p. 243 |
| First Moment Intensity and Janossy Densities | p. 243 |
| MMIF: Marked Multitarget Intensity Filter | p. 245 |
| Target Modeling | p. 245 |
| Joint Measurement-Target Intensity Function | p. 246 |
| Likelihood Function | p. 248 |
| MMIF Recursion | p. 250 |
| Linear Filter Model | p. 253 |
| PPP Signal Model | p. 253 |
| Poisson Limit | p. 254 |
| Utility | p. 256 |
| Glossary | p. 257 |
| List of Acronyms | p. 263 |
| References | p. 265 |
| Index | p. 271 |
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