%PDF-1.3 1 0 obj << /Kids [ 4 0 R 5 0 R 6 0 R 7 0 R 8 0 R 9 0 R 10 0 R 11 0 R 12 0 R ] /Type /Pages /Count 9 >> endobj 2 0 obj << /Subject (Neural Information Processing Systems http\072\057\057nips\056cc\057) /Publisher (Curran Associates\054 Inc\056) /Language (en\055US) /Created (2015) /EventType (Spotlight) /Description-Abstract (In many statistical problems\054 a more coarse\055grained model may be suitable for population\055level behaviour\054 whereas a more detailed model is appropriate for accurate modelling of individual behaviour\056 This raises the question of how to integrate both types of models\056 Methods such as posterior regularization follow the idea of generalized moment matching\054 in that they allow matchingexpectations between two models\054 but sometimes both models are most conveniently expressed as latent variable models\056 We propose latent Bayesian melding\054 which is motivated by averaging the distributions over populations statistics of both the individual\055level and the population\055level models under a logarithmic opinion pool framework\056 In a case study on electricity disaggregation\054 which is a type of single\055channel blind source separation problem\054 we show that latent Bayesian melding leads to significantly more accurate predictions than an approach based solely on generalized moment matching\056) /Producer (PyPDF2) /Title (Latent Bayesian melding for integrating individual and population models) /Date (2015) /ModDate (D\07220151218142756\05508\04700\047) /Published (2015) /Type (Conference Proceedings) /firstpage (3617) /Book (Advances in Neural Information Processing Systems 28) /Description (Paper accepted and presented at the Neural Information Processing Systems Conference \050http\072\057\057nips\056cc\057\051) /Editors (C\056 Cortes and N\056D\056 Lawrence and D\056D\056 Lee and M\056 Sugiyama and R\056 Garnett and R\056 Garnett) /Author (Mingjun Zhong\054 Nigel Goddard\054 Charles Sutton) /lastpage (3625) >> endobj 3 0 obj << /Type /Catalog /Pages 1 0 R >> endobj 4 0 obj << /Contents 13 0 R /Parent 1 0 R /Resources 14 0 R /MediaBox [ 0 0 612 792 ] /Annots [ 44 0 R 45 0 R 46 0 R 47 0 R 48 0 R ] /Type /Page >> endobj 5 0 obj << /Contents 49 0 R /Parent 1 0 R /Resources 50 0 R /MediaBox [ 0 0 612 792 ] /Annots [ 67 0 R 68 0 R 69 0 R 70 0 R 71 0 R 72 0 R 73 0 R 74 0 R 75 0 R 76 0 R 77 0 R 78 0 R 79 0 R 80 0 R 81 0 R 82 0 R 83 0 R 84 0 R 85 0 R ] /Type /Page >> endobj 6 0 obj << /Contents 86 0 R /Parent 1 0 R /Resources 87 0 R /MediaBox [ 0 0 612 792 ] /Annots [ 96 0 R 97 0 R 98 0 R 99 0 R 100 0 R 101 0 R 102 0 R 103 0 R ] /Type /Page >> endobj 7 0 obj << /Contents 104 0 R /Parent 1 0 R /Resources 105 0 R /MediaBox [ 0 0 612 792 ] /Annots [ 114 0 R 115 0 R 116 0 R 117 0 R 118 0 R 119 0 R 120 0 R 121 0 R 122 0 R ] /Type /Page >> endobj 8 0 obj << /Contents 123 0 R /Parent 1 0 R /Resources 124 0 R /MediaBox [ 0 0 612 792 ] /Annots [ 125 0 R 126 0 R 127 0 R 128 0 R 129 0 R 130 0 R 131 0 R ] /Type /Page >> endobj 9 0 obj << /Contents 132 0 R /Parent 1 0 R /Resources 133 0 R /MediaBox [ 0 0 612 792 ] /Annots [ 134 0 R 135 0 R 136 0 R 137 0 R 138 0 R 139 0 R 140 0 R 141 0 R ] /Type /Page >> endobj 10 0 obj << /Contents 142 0 R /Parent 1 0 R /Resources 143 0 R /MediaBox [ 0 0 612 792 ] /Annots [ 148 0 R 149 0 R 150 0 R ] /Type /Page >> endobj 11 0 obj << /Contents 151 0 R /Parent 1 0 R /Resources 152 0 R /MediaBox [ 0 0 612 792 ] /Annots [ 153 0 R 154 0 R 155 0 R ] /Type /Page >> endobj 12 0 obj << /Contents 156 0 R /Parent 1 0 R /Type /Page /Resources 157 0 R /MediaBox [ 0 0 612 792 ] >> endobj 13 0 obj << /Length 2900 /Filter /FlateDecode >> stream xڅَܸ}ߢ