- Bayesian networks are probabilistic graphical models that represent conditional dependencies between random variables through a directed acyclic graph. Each node corresponds to a random variable, and edges represent conditional probabilities. - There are two main ways to build a Bayesian network - manually defining the graph structure and conditional probabilities, or automatically learning the structure from data. Bayesian networks can handle incomplete data and help uncover causal relationships. - Bayesian networks have advantages like explaining relationships visually, handling missing data, and combining data with expert knowledge. However, they are difficult to design and don't work as well with high dimensional data. An example shows using disease symptoms to predict disease probabilities.

1. WOLLO UNIVERSITY
COLLEGE OF MEDICINE AND HEALTH SCIENCES
SCHOOL OF PUBLIC HEALTH
DEPARTEMENT OF HEALTH INFORMATICS
GROUP ASSAGNMENT OF HEALTH DATA ANAYTICS
SUBMITED TO: MULUGETA HAYLOM
SUBMITION DATE:JULY 7 2022 G.C
ETHIOPIAN ,DESSISE 2022
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2. GROUP MEMBER
1,ALEMU MEHARIW ……………………………………3473/11
2,TADESE ALEMU……………………………………….3680/11
3,TESFAHUN ASMARE…………………………………3689/11
4,KALKIDAN WASE ……………………………………..
5,KIDANEMARYAM ABRHAM ………………………3595/11
6,TIGIST AYALNEH ……………………………………………3693/11
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3. Objectives
• Define Probabilistic models
• Define Bayesian Networks
• Discus the feature of Bayesian Networks?
• What are the important of Bayesian Networks?
• How to build a Bayesian network?
• How are Bayesian networks implemented?
• Advantage and disadvantage of Bayesian Networks?
• Example
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4. INTRODUCTION
• Probabilistic modeling is a statistical technique used to take
into account the impact of random events or actions in
predicting the potential occurrence of future outcomes.
• In statistics, Probabilistic models are used to define a
relationship between variables.
• can be used to calculate the probabilities of each variable.
• The only way is to develop a model that can preserve the
conditional dependencies between random variables and
conditional independence in other cases.
• This leads us to the concept of Bayesian Networks.
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5. Bayesian Networks
• is a probabilistic graphical model for representing knowledge
about an uncertain domain.
• The network consists node and edge.
• Nodes: Random variables in a graphical model.
• Edges: Relationships between random variables in a graphical
model.
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6. Cont…
• each node corresponds to a random variable
and each edge represents the conditional
probability for the corresponding random
variables.
• It capture both conditionally dependent and
conditionally independent relationships
between random variables.
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7. Cont…
• Bayesian Networks help us to effectively visualize the
probabilistic model for each domain and to study the
relationship between random variables in the form of a user-
friendly graph.
• It represents a set of variables and its conditional probabilities
with a Directed Acyclic Graph (DAG).
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8. Cont….
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9. Features of BN
• BN techniques have several features that make them useful in
many real-life data analysis and management questions.
 They provide a natural way to handle missing data
 they allow combination of data with domain knowledge
 they facilitate learning about causal relationships between
variables.
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10. Cont…
 they provide a method for avoiding overfitting of data
 they can show good prediction accuracy even with rather
small sample sizes
 they can be easily combined with decision analytic tools to aid
management
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11. Uses
• Bayesian network is an important part of machine learning
and statistics.
• It is used in data mining and scientific discovery.
• tool for analyzing the past, predicting the future and
improving the quality of decisions.
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12. Cont….
Bayesian networks can readily handle incomplete
data sets.
 When one of the inputs is not observed, many models will
produce an inaccurate prediction, because they do not
encode the correlation between the input variables. Bayesian
networks offer a natural way to encode such dependencies.
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13. cont…
Bayesian networks allow one to learn about
causal relationships
• For example, a marketing analyst may want to know whether
or not it is worthwhile to increase exposure of a particular
advertisement in order to increase the sales of a product.
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14. Cont..
• Visualization. The model provides a direct way to visualize the
structure of the model and motivate the design of new
models.
• Relationships. Provides insights into the presence and
absence of the relationships between random variables.
• Computations. Provides a way to structure complex
probability calculations.
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15. How to build a Bayesian network?
• There are two ways to build a Bayesian network.
 a manual construction
 Automatic(computer) construction (so called "learning") from
databases
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16. Manual construction
• Manual construction of a Bayesian network assumes prior
expert knowledge of the underlying domain.
• The first step is to build a directed acyclic graph
• The second step to assess the conditional probability
distribution in each node.
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17. Directed acyclic graph
• Building the directed acyclic graph starts with
identification of relevant nodes (random
variables) and structural dependence among
them.
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18. Conditional probability distribution
• The constructed directed acyclic graph has to
include conditional probability distributions
for every node in the graph.
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19. Automatic learning
• Unlike manual construction, automatic learning does not
require expert knowledge of the underlying domain.
• Bayesian networks may be learnt automatically straight from
databases using experience-based algorithms often built-in in
appropriate software
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20. How are Bayesian networks implemented?
• A Bayesian network is a graphical model where each of the
nodes represent random variables.
• Each node is connected to other nodes by directed arcs.
• Each arc represents a conditional probability distribution of
the parents given the children.
• The directed edges represent the influence of a parent on its
children.
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21. Cont..
• The nodes usually represent some real-world objects and the
arcs represent some physical or logical relationship between
them.
• Bayesian networks are used in many applications like
automatic speech recognition, document/image classification,
medical diagnosis, and robotics.
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22. Advantage
• It is readable to both computers and humans; both can
interpret the information.
• unlike some networks like neural networks, which humans
can’t read.
• it is an excellent network for adding a new piece of data to an
existing probabilistic model.
• Computations calculate complex probability problems
efficiently.
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23. Disadvantage
• The Bayesian network fails to define cyclic relationships.
• The network is expensive to build.
• The design of Bayesian Networks is hard to make compared to
other networks. It needs a lot of effort.
• It performs poorly on high dimensional data.
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24. Example
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]
 For example, with a given symptom we can predict the
probability of a disease occurring with several other factors
contributing to the disease.
 In the below diagram A, B, C and are 3 random variables
represented by nodes given in the network of the graph. To
node B is its parent node and C and A is its child node

25. Cont…
• Consider a problem with three random variables: A, B, and C. A is
dependent upon B, and C is dependent upon B.
 We can state the conditional dependencies as follows:
 A is conditionally dependent upon B, e.g. P(A|B)
 C is conditionally dependent upon B, e.g. P(C|B)
 We know that C and A have no effect on each other.
 We can also state the conditional independencies as follows:
 A is conditionally independent from C: P(A|B, C)
 C is conditionally independent from A: P(C|B, A)
• Notice that the conditional dependence is stated in the presence of
the conditional independence. That is, A is conditionally
independent of C, or A is conditionally dependent upon B in the
presence of C.
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