Bayesian Networks and Decision GraphsThe author also:- provides a well-founded practical introduction to Bayesian networks, decision trees and influence diagrams;- gives several examples and exercises exploiting the computer systems for Bayesian netowrks and influence diagrams;- gives practical advice on constructiong Bayesian networks and influence diagrams from domain knowledge;- embeds decision making into the framework of Bayesian networks;- presents in detail the currently most efficient algorithms for probability updating in Bayesian networks;- discusses a wide range of analyes tools and model requests together with algorithms for calculation of responses;- gives a detailed presentation of the currently most efficient algorithm for solving influence diagrams. |
Contents
III | 5 |
VII | 6 |
VIII | 8 |
IX | 12 |
X | 13 |
XII | 14 |
XIII | 15 |
XV | 17 |
LXXIX | 111 |
LXXXI | 112 |
LXXXII | 115 |
LXXXIII | 116 |
LXXXV | 118 |
LXXXVII | 120 |
LXXXVIII | 121 |
LXXXIX | 122 |
XVI | 19 |
XVII | 20 |
XVIII | 22 |
XIX | 23 |
XX | 24 |
XXI | 25 |
XXII | 26 |
XXIII | 27 |
XXIV | 29 |
XXV | 30 |
XXVI | 32 |
XXVIII | 37 |
XXXI | 38 |
XXXII | 40 |
XXXIII | 41 |
XXXIV | 42 |
XXXV | 43 |
XXXVI | 45 |
XXXVII | 46 |
XXXIX | 48 |
XL | 52 |
XLI | 54 |
XLII | 56 |
XLIII | 57 |
XLIV | 59 |
XLV | 61 |
XLVI | 63 |
XLVII | 64 |
XLVIII | 66 |
XLIX | 68 |
L | 70 |
LI | 71 |
LII | 72 |
LIII | 73 |
LIV | 74 |
LV | 75 |
LVI | 76 |
LVIII | 81 |
LX | 82 |
LXI | 83 |
LXII | 84 |
LXIII | 85 |
LXIV | 86 |
LXV | 89 |
LXVI | 90 |
LXVII | 91 |
LXVIII | 92 |
LXIX | 93 |
LXX | 94 |
LXXI | 95 |
LXXII | 97 |
LXXIII | 99 |
LXXIV | 100 |
LXXV | 103 |
LXXVI | 104 |
LXXVII | 106 |
LXXVIII | 107 |
XC | 124 |
XCII | 127 |
XCIII | 130 |
XCIV | 135 |
XCV | 137 |
XCVI | 138 |
XCVII | 139 |
XCVIII | 142 |
XCIX | 144 |
C | 147 |
CI | 149 |
CII | 153 |
CIV | 159 |
CVII | 161 |
CIX | 164 |
CX | 167 |
CXI | 168 |
CXII | 171 |
CXIII | 174 |
CXIV | 176 |
CXV | 179 |
CXVI | 181 |
CXVII | 182 |
CXIX | 184 |
CXX | 186 |
CXXI | 189 |
CXXII | 191 |
CXXIII | 194 |
CXXIV | 195 |
CXXV | 203 |
CXXVII | 204 |
CXXVIII | 205 |
CXXX | 207 |
CXXXI | 210 |
CXXXIII | 211 |
CXXXV | 212 |
CXXXVI | 213 |
CXXXVII | 215 |
CXXXVIII | 218 |
CXXXIX | 221 |
CXL | 224 |
CXLI | 225 |
CXLII | 227 |
CXLIV | 229 |
CXLV | 230 |
CXLVI | 237 |
CXLVII | 238 |
CXLVIII | 240 |
CXLIX | 243 |
CLI | 247 |
CLII | 248 |
CLIII | 253 |
255 | |
CLVI | 257 |
265 | |
Other editions - View all
Bayesian Networks and Decision Graphs Thomas Dyhre Nielsen,FINN VERNER JENSEN No preview available - 2010 |
Common terms and phrases
action algorithm analysis Angina assume Bayes Bayesian network Bucket elimination calculate called causal network certainty chain rule chance node clean clique Cold Conditional independence conditional probabilities configuration conflict Consider Construct d-separated D₁ decision nodes decision tree direction domain graph elimination order evidence example Exercise expected utility Fever fill-ins given graph in Figure hidden Markov model hypothesis variable IEJ tree impact independent infected Infi influence diagram initial instantiated join tree joint probability table Let BN marginalizing maximal messages method milk model in Figure moral graph network in Figure observed optimal P(Angina P(BA P(he pa(A parameters parents perfect elimination sequence perform posterior probabilities prior probabilities propagation Proposition sample Section set of potentials simplicial nodes slice sore throat spark plugs strategy strong junction tree structure subset t₁ Theorem triangulated graph troubleshooting undirected graph utility nodes V₁ yields