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Dempster Shafer Theory
By
Md. Fazle Rabbi
16CSE057
4.2
Contents
⮚ Definition
⮚ Why DS theory
⮚ Example
⮚ Characteristic
⮚ Advantage
4.3
Definition
The theory of belief functions, also referred to as
evidence theory or Dempster–Shafer theory, is a general
framework for reasoning with uncertainty, with
understood connections to other frameworks such as
probability, possibility and imprecise probability theories.
4.4
Why DS Theory?
• Bayesian Theory is only concerned about single
evidence.
• Bayesian Theory can not describe ignorance.
4.5
Example
4.6
Let us comsider a room where 4 persons are
presented
Figure 1 Example
4.7
Figure 2 Example
4.8
Figure 2 Example
Who is the murderer ?
4.9
To Solve this, let us consider some following
possibilities
• Either {A} or {C} or {D} have killed him.
• Either {A,C} or {C,D} or {A,D} have killed him.
• Or the three of them have killed him i.e:{A,C,D}
• None of them killed him.
Using the above example we can say:
Set of possible conclusion (P)=(P1,P2….Pn)
Power set will contain 2n elements where n is number of
elements of the possible set.
If P={a,b,c}, then power set is given as
{0,{a},{b},{c},{a,b},{a,c},{b,c},{a,b,c} = 2^3 elements.
4.10
The Characteristic in this model
● Ignorance is reduced in this theory by adding
more and more evidences.
● Combination rule is used to combine various
types of possibilities.
4.11
Advantage
● As we add more information uncertainty interval
reduces.
● DST has much lower level of ignorance
● Diagnose hierarchies can be represented using
this
● Person dealing with such problems is free to
think about evidences.
4.12
Thank You

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3. dempster combinations

  • 1. Dempster Shafer Theory By Md. Fazle Rabbi 16CSE057
  • 2. 4.2 Contents ⮚ Definition ⮚ Why DS theory ⮚ Example ⮚ Characteristic ⮚ Advantage
  • 3. 4.3 Definition The theory of belief functions, also referred to as evidence theory or Dempster–Shafer theory, is a general framework for reasoning with uncertainty, with understood connections to other frameworks such as probability, possibility and imprecise probability theories.
  • 4. 4.4 Why DS Theory? • Bayesian Theory is only concerned about single evidence. • Bayesian Theory can not describe ignorance.
  • 6. 4.6 Let us comsider a room where 4 persons are presented Figure 1 Example
  • 8. 4.8 Figure 2 Example Who is the murderer ?
  • 9. 4.9 To Solve this, let us consider some following possibilities • Either {A} or {C} or {D} have killed him. • Either {A,C} or {C,D} or {A,D} have killed him. • Or the three of them have killed him i.e:{A,C,D} • None of them killed him. Using the above example we can say: Set of possible conclusion (P)=(P1,P2….Pn) Power set will contain 2n elements where n is number of elements of the possible set. If P={a,b,c}, then power set is given as {0,{a},{b},{c},{a,b},{a,c},{b,c},{a,b,c} = 2^3 elements.
  • 10. 4.10 The Characteristic in this model ● Ignorance is reduced in this theory by adding more and more evidences. ● Combination rule is used to combine various types of possibilities.
  • 11. 4.11 Advantage ● As we add more information uncertainty interval reduces. ● DST has much lower level of ignorance ● Diagnose hierarchies can be represented using this ● Person dealing with such problems is free to think about evidences.