人工智能代写 | SIT743 Bayesian Learning and Graphical Models

这个作业是为指定的场景进行贝叶斯学习和创建图形模型
SIT743 Bayesian Learning and Graphical Models
Assignment-2
INSTRUCTIONS:
• For this assignment, you need to submit the following TWO files.
1. A written document (A single pdf only) covering all of the items described in the
questions. All answers to the questions must be written in this document, i.e, not in
the other files (code files) that you will be submitting. All the relevant results
(outputs, figures) obtained by executing your R code must be included in this
document.
For questions that involve mathematical formulas, you may write the answers
manually (hand written answers), scan it to pdf and combine with your answer
document. Submit a combined single pdf of your answer document.
2. A separate “.R” file or ‘.txt’ file containing your code (R-code script) that you
implemented to produce the results. Name the file as “name-StudentID-Ass2-
Code.R” (where `name’ is replaced with your name – you can use your surname or
first name, and StudentID with your student ID).
• All the documents and files should be submitted (uploaded) via SIT 743 Clouddeakin
Assignment Dropbox by the due date and time.
• Zip files are NOT accepted. All two files should be uploaded separately to the
CloudDeakin.
• E-mail or manual submissions are NOT allowed. Photos of the document are NOT
allowed.
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Assignment tasks
Q1) [31 Marks]
Weather conditions influence the production of good quality coffee in a region. A list
of factors that influence the coffee cultivation, along with their possible values, and a
Bayesian network that represents the relationship between these factors (variables) are
given below.
M (Maximum Temperature) ∈ { < 20, 20-30, 30-40, > 40 }
N (Minimum Temperature) ∈ { < 0, 0-10, 10-20, > 20}
W (Wind speed) ∈ {Low, Medium, High}
H (Relative humidity) ∈ {< 50, 50-60, > 60}
R (Precipitation) ∈ {Low, High}
Page 2 of 8
S (Solar radiation) ∈ {Low, Medium, High}
1.1) Write down the joint distribution , , , ,
,  for the above
network.
1.2) Find the minimum number of parameters required to fully specify the
distribution according to the above network.
1.3)
a) Write down a joint probability density function if there are no
independence among the variables is assumed.
b) How many parameters are required, at a minimum, if there are no
independencies among the variables is assumed?
c) Compare with the result of the above question (Q1.2) and comment.
1.4) d-separation method can be used to find two sets of independent or
conditionally independent variables in a Bayesian network. For each of the
statements given below from (a) to (c), perform the following:
• List all the possible paths from the first (set of) node/s to the second (set
of) node/s.
• State if each of those paths is blocking or non-blocking with reasons.
• Hence, mention if the statement is true or false.
a) ⊥ S | ∅ (M is marginally independent of S)
b)  ⊥ R | N, H} (W is conditionally independent of R given {N, H})
c) 
,  ⊥ W | H
Page 3 of 8
1.5) Write a R-Program to produce the above Bayesian network, and perform the
d-separation tests for all of the above cases mentioned in Q1.4 (a) to (c). Show
the plot of the network you obtained and the output (of d-separation test)
from your program.
1.6)
a) Show the step by step process to perform variable elimination to
compute  |   ,    . Use the following variable ordering
for the elimination process:
N, H, M.
b) What is the treewidth of the network, given the above elimination ordering?

[Marks 2+4+5+10+3+7 = 31]
Q2) [16 Marks] Implementing a Bayesian network in R and performing inference
A belief network models the relation between the variables A, B, C, D and E, which
represents the season, river flow rate, fish species, color and size respectively. Each
variable takes different states as given below.
  !” #$ ∈ %!&, ‘()
* (+,!( -.#% (“&! ∈ .#%, ℎ+0ℎ
1 -+ ℎ 2!3+! ∈ 4” , 5#’
6 3#.#7( ∈ .+0ℎ&, 8!’+78, ‘”(9
:  +;! ∈ %+’!, &ℎ+$
The belief network that models these variables has (probability) tables as shown below.
Page 4 of 8
2.1) Use the below libraries in R to create this belief network in R along with the
probability values, as shown in the above table.
You may use the following libraries for this:
#https://www.bioconductor.org/install/
#BiocManager::install(c(“gRain”, “RBGL”, “gRbase”))
#BiocManager::install(c(“Rgraphviz”))
library(“Rgraphviz”)
library(RBGL)
library(gRbase)
library(gRain)
#define the appropriate network and use the
“compileCPT()”function to Compile list of conditional
probability tables, and create the network.
a) Show the obtained belief network for this distribution
b) Show the probability tables obtained from the R output, (and verify with
the above table).
2.2) Use R program to compute the following probabilities:
a) Given that the river flow rate is low, what is the probability that size is thin?
b) Given that the colour is dark and the season is dry, what is the probability
that the fish species is Cod?
c) Find the joint distribution of colour and fish species.
d) Find the marginal distribution of fish species.
[Marks: (3+5) + (2+2+2+2) = 16]
Q3) [15 Marks]
Consider four binary variables A, B, C, D. The Directed Acyclic Graph (DAG) shown
below describes the relationship between these variables along with their conditional
probability tables (CPT).
Page 5 of 8
3.1) In the above network, state why A is independent of B with reasons, i.e., A⊥B.
3.2) Hence, obtain an expression (in a simplified form) for 6  >|  >, *  > in
terms of ? only.
3.3) The table shown below provides 20 simulated data obtained for the above Bayesian
network. Use this data to find the maximum likelihood estimates of @, ?, A and
B.
3.4) Find the value of 6  >|  >, *  > using the values obtained for ? from
the above question Q3.3.
[Marks 3+ 7 + 4 + 1 = 15]
Page 6 of 8
Q4) Bayesian Structure Learning [27 Marks]
For this question, you will be using a dataset, called “hailfinder” available from the
‘bnlearn’ R package. which contains 56 variables. This has meteorological data.
Use the following R code to load the hailfinder dataset:
library (bnlearn)
# load the data.
data(hailfinder)
summary(hailfinder)
The true network structure of this dataset can be viewed (plot) using the following R
code.
library(bnlearn)
# create and plot the network structure.
modelstring = paste0(“[N07muVerMo][SubjVertMo][QGVertMotion][SatContMoist][RaoContMoist]”,
“[VISCloudCov][IRCloudCover][AMInstabMt][WndHodograph][MorningBound][LoLevMoistAd][Date]”,
“[MorningCIN][LIfr12ZDENSd][AMDewptCalPl][LatestCIN][LLIW]”,
“[CombVerMo|N07muVerMo:SubjVertMo:QGVertMotion][CombMoisture|SatContMoist:RaoContMoist]”,
“[CombClouds|VISCloudCov:IRCloudCover][Scenario|Date][CurPropConv|LatestCIN:LLIW]”,
“[AreaMesoALS|CombVerMo][ScenRelAMCIN|Scenario][ScenRelAMIns|Scenario][ScenRel34|Scenario]”,
“[ScnRelPlFcst|Scenario][Dewpoints|Scenario][LowLLapse|Scenario][MeanRH|Scenario]”,
“[MidLLapse|Scenario][MvmtFeatures|Scenario][RHRatio|Scenario][SfcWndShfDis|Scenario]”,
“[SynForcng|Scenario][TempDis|Scenario][WindAloft|Scenario][WindFieldMt|Scenario]”,
“[WindFieldPln|Scenario][AreaMoDryAir|AreaMesoALS:CombMoisture]”,
“[AMCINInScen|ScenRelAMCIN:MorningCIN][AMInsWliScen|ScenRelAMIns:LIfr12ZDENSd:AMDewptCalPl]”,
“[CldShadeOth|AreaMesoALS:AreaMoDryAir:CombClouds][InsInMt|CldShadeOth:AMInstabMt]”,
“[OutflowFrMt|InsInMt:WndHodograph][CldShadeConv|InsInMt:WndHodograph][MountainFcst|InsInMt]”,
“[Boundaries|WndHodograph:OutflowFrMt:MorningBound][N34StarFcst|ScenRel34:PlainsFcst]”,
“[CompPlFcst|AreaMesoALS:CldShadeOth:Boundaries:CldShadeConv][CapChange|CompPlFcst]”,
“[InsChange|CompPlFcst:LoLevMoistAd][CapInScen|CapChange:AMCINInScen]”,
“[InsSclInScen|InsChange:AMInsWliScen][R5Fcst|MountainFcst:N34StarFcst]”,
“[PlainsFcst|CapInScen:InsSclInScen:CurPropConv:ScnRelPlFcst]”)
dag = model2network(modelstring)
par(mfrow = c(1,1))
#BiocManager::install(c(“Rgraphviz”))
graphviz.plot(dag)
Page 7 of 8
Use R programming, as appropriate, to answers the following questions.

4.1) Use the hailfinder dataset to learn Bayesian network structures using hillclimbing (hc) algorithm, utilizing two different scoring methods, namely
Bayesian Information Criterion score (BIC score) and the Bayesian Dirichlet
equivalent (Bde score), for each of the following sample sizes of the data:
a) 100 (first 100 data)
b) 1000 (first 1000 data)
c) 10000 (first 10000 data)
For each of the above cases,
• provide the scores obtained for BIC and BDe,
• Plot the network structure obtained for the BIC and BDe scores.
4.2) Based on the results obtained for the above question (Q 4.1), discuss how the BIC
score compare with BDe score for different sample sizes in terms of structure
and score of the learned network.
4.3)
a) Find the Bayesian network structures utilising the full dataset, and using
both BIC and Bde scores. Show the scores and the obtained networks.
b) Compare the networks obtained above (in Q4.3.a) for each BIC and Bde
scoring methods with the true network structure and comment. Use the
“compare()” function and “graphviz.compare()” function available in the
“bnlearn” R package to perform these comparisons and comment.
c) Fit the data to the network obtained using the BIC score in the above question
(Q4.3.a) in order to compute the conditional probability distribution table
entries (CPD table values). Show the obtained CPD table entries for the
variable “CombClouds”.
d) Use the above learned network obtained (in Q4.3.c) to find the probability of :
P(CombClouds =”Cloudy” | MeanRH = “VeryMoist”, IRCloudCover =”Cloudy”)

[Marks (3*4) + 3 + (4+3+3+2) = 27]
Page 8 of 8
Q5) Research based questions (Practical applications in real world) [11 Marks]
a) Download the following article from the link provided below. Read that article and
answer the following questions. This article provides a real life case study on creating
and using a Bayesian network for road accident data analysis.
Ali Karimnezhad & Fahimeh Moradi (2017), Road accident data analysis using
Bayesian networks, Transportation Letters, 9:1, 12-19,
DOI: 10.1080/19427867.2015.1131960
Web: https://www.tandfonline.com/doi/full/10.1080/19427867.2015.1131960
Note that you will be able to download this paper via Deakin library using your Deakin
credentials (username and password).
(https://www.deakin.edu.au/library/help/add-browser-bookmarklet)
i) Describe the dataset used for their analysis. What are the variables used? Are
the variables numerical or categorical or mixed? How many records of data have
been used?
ii) What is the name of the algorithm used for learning the Bayesian network
structure?
iii) What software tool have been used to build and visualize the Bayesian network?
Provide a web link to that software.
iv) Read the section titled “Parameter learning in the road accident network” in
that paper and extract the following probability values that they have computed,
and mention them:
I. The probability of being injured while wearing seat belt and driving a
car, knowing that the driver has a diploma degree and a type 2 driving
license.
II. The probability of death while wearing seat belt and driving a car,
knowing that the driver has a diploma degree and a type 2 driving license
III. The probability of being injured while not wearing the seatbelt, knowing
that the driver has a diploma degree and a type 2 driving license
IV. The probability of death while not wearing the seatbelt, knowing that
the driver has a diploma degree and a type 2 driving license
V. Based on the probability values obtained above, what conclusions are
made?
b) Do a research (using journal or conference papers/publications) and describe ONE
other real-world application of any Bayesian methods/Bayesian networks. Your
description should include the following:
i) Briefly describe on your own words what the application is about.
ii) The details of the techniques used.
Provide references for the applications/papers used. Description for this question
Q5(b) should NOT exceed 400 words (including references).
NOTE: Your answers for all of the above questions must be written in your own words.
Copying directly from the paper/reference text will constitute to Plagiarism and zero
marks.
[Marks 7 + 4 =11]