SlideShare a Scribd company logo
Discrete Stochastic
Processes
statisticshomeworkhelper.com
For any help regarding Stochastic Processes Homework Help
Visit :- https://www.excelhomeworkhelp.com/,
Email :- info@excelhomeworkhelp.com or
Call us at :- +1 678 648 4277
Circle the name of your Tutorial Instructor:
Ashish Carole Christos Eric
George Jack Nick Tina
• This quiz is closed book. There is an Appendix giving the definitions of standard
properties of relations.
• There are four (4) problems totaling 100 points. Problems are labeled with their point
values.
• Put your name on the top of every page – these pages may be separated for grading.
• Write your solutions in the space provided. If you need more space, write on the back
of the sheet containing the problem. Please keep your entire answer to a problem on
that problem’s page.
• You may assume any of the results presented in class or in the lecture notes.
• Be neat and write legibly. You will be graded not only on the correctness of your
answer, but also on the clarity with which you express it.
Problems
statisticshomeworkhelper.com
Problem 1 Consider the following system specifications1.
1. The system is in multiuser state if it is operating normally.
2. If the system is operating normally, then the kernel is functioning.
3. The kernel is not functioning or the system is in interrupt mode.
4. If the system is not in multiuser state, then it is in interrupt mode.
5. The system is not in interrupt mode.
(a) To make sense of these confusing conditions, let’s introduce four Boolean variables.
statisticshomeworkhelper.com
M ::= in Multiuser state (1)
N ::= operating Normally (2)
K ::= Kernel is functioning (3)
I ::= in Interrupt mode (4)
Translate the five statements in the specification into propositional
logic notation: ∧,∨,¬, −→
statisticshomeworkhelper.com
(b) Are these system specifications consistent? . Prove it!
Problem 2
For each of the following logical formulas with domain of discourse the natural numbers,
N, indicate whether it is a possible formulation of I:
the Induction Axiom,
S: the Strong Induction Axiom,
L: the Least Number Principle (also known as Wellordering),
or N: None of these.
For example, the ordinary Induction Axiom could be expressed by the following formula, so
it gets labelled “I”.
statisticshomeworkhelper.com
This is a multiple choice problem: do not explain your answer.
(a) (P(b) ∧ [∀k ≥ b P(k) −→ P(k + 1)]) −→ ∀k ≥ b P(k)
(b) (P(b) ∧ [∀k ≤ b P(k) −→ P(k + 1)]) −→ ∀k ≤ b P(k)
(c) [∀b (∀k < b P(k)) −→ P(b)] −→ ∀k P(k) (
(d) (∃n P(n)) −→ ∃n ∀k < n P(k)
(e) ∀n [P(n) −→ (∃n P(n) ∧ ∀k < n P(k))]
Problem 3 Classify each of the following binary relations as
E: An equivalence relation.
T: A Total order,
P: A Partial order that is not total.
S: A Symmetric relation that is not transitive.
N: None of the above..
statisticshomeworkhelper.com
This is a multiple choice problem: do not explain your answer.
(a) The relation xRy between times of day such that x and y are at most twenty minutes
apart.
(b) The relation xRy between times of day such that x is more than twenty minutes later
than y.
(c) The relation xRy over all words in this sentence such that x does not appear after y.
(Consider “x”, “y”, and “xRy” to be words.)
(d) The relation xRy over all words in this sentence such that word x does not appear
after word y.
(e) The relation xRy over all words in this sentence such that the final appearance of y
occurs after x.
Problem 4 To encourage collaborative study, the 6.042 staff is considering assigning
each student to a study group with two or three other students. Prove that as long as the
enrollment is large enough, the class can always be divided into such study groups.
statisticshomeworkhelper.com
Appendix
A binary relation, R, on a set A is
• reflexive iff xRx,
• symmetric iff xRy −→ yRx,
• antisymmetric iff xRy ∧ yRx −→ x = y,
• transitive iff xRy ∧ yRz −→ xRz,
for all x, y, z ∈ A.
• R is an equivalence relation iff it is reflexive, symmetric and transitive.
• R is a partial order iff it is transitive and antisymmetric.
• R is a total order iff it is a partial order and for all x = y ∈ A either xRy or yRx.
statisticshomeworkhelper.com
Problem 1 Consider the following system specifications1.
1. The system is in multiuser state iff it is operating normally.
2. If the system is operating normally, then the kernel is functioning.
3. The kernel is not functioning or the system is in interrupt mode.
4. If the system is not in multiuser state, then it is in interrupt mode.
5. The system is not in interrupt mode.
(a) To make sense of these confusing conditions, let’s introduce four Boolean
variables.
M ::= in Multiuser state (1)
N ::= operating Normally (2)
K ::= Kernel is functioning (3)
I ::= in Interrupt mode (4)
statisticshomeworkhelper.com
Translate the five statements in the specification into propositional logic notation: ∧,∨,¬,
−→ ,←→
Solution.
M N ←→ (5)
N −→ K (6)
¬K ∨ I (7)
¬M −→ I (8)
¬I (9)
(b) (0 points) Are these system specifications consistent? . Prove it!
Solution. There are several ways to approach this problem. One is to construct a truth
table with sixteen lines—one for each way of assigning truth values to the four variables
M, N, K, and I.
We can avoid the cumbersome truthtable if we reason by cases. Case 1: I is true. Then
the last formula (9) is false, and the whole specification is false. Case 2: I is false. Now
formula (8) can be true only if ¬M is false, that is, only if M is true. Likewise, formula (7)
can be true only if ¬K is true, that is, K is false.
Since K is false, formula (6) can be true only if N is false. Thus, we have deduced that in
order to be consistent in this case, we must have
statisticshomeworkhelper.com
I = false
M = true
K = false
N = false.
But now formula (5) is false, so it is impossible for all the formulas to be true: the system is
inconsistent.
Problem 2 For each of the following logical formulas with domain of discourse the
natural numbers, N, indicate whether it is a possible formulation of
I: the Induction Axiom,
S: the Strong Induction Axiom,
L: the Least Number Principle (also known as Wellordering), or
N: None of these.
For example, the ordinary Induction Axiom could be expressed by the following formula, so
it gets labelled “I”.
(P(0) ∧ [∀k P(k) −→ P(k + 1)]) −→ ∀k P(k) I
statisticshomeworkhelper.com
This is a multiple choice problem: do not explain your answer.
(a) (P(b) ∧ [∀k ≥ b P(k) −→ P(k + 1)]) −→ ∀k ≥ b P(k)
Solution. I. This is a perfect formulation of the Induction Axiom. b is used for the base
case; P(k) −→ P(k + 1) is the inductive case.
(b) (P(b) ∧ [∀k ≤ b P(k) −→ P(k + 1)]) −→ ∀k ≤ b P(k) Solution. N. The two occurences
of k <= b should have been k >= b
(c) [∀b (∀k < b P(k)) −→ P(b)] −→ ∀k P(k) Solution. S. Since you are assuming P(k)
for all k < b, this is strong induction.
(d) (∃n P(n)) −→ ∃n ∀k < n P(k) Solution. N. This statement is in fact always true;
when n = 0, ∀k < n P(k). It should say P(n) ∧ ∀k < n; P(k)
(e) ∀n [P(n) −→ (∃n P(n) ∧ ∀k < n P(k))] Solution. L. This is a valid formulation; it does
not have that problem in (2d).
Problem 3 . Classify each of the following binary relations as
statisticshomeworkhelper.com
E: An equivalence relation.
T: A Total order,
P: A Partial order that is not total
S: A Symmetric relation that is not transitive.
N: None of the above.
This is a multiple choice problem: do not explain your answer.
(a) The relation xRy between times of day such that x and y are at most twenty
minutes apart.
Solution. S: This relation is reflexive and symmetric. It is not transitive; Consider the
counterexample: 1:00R 1:15, 1:15R 1:22 but ¬1:00R 1:22.
(b) The relation xRy between times of day such that x is more than twenty minutes later
than y.
Solution. P: This relation is antisymmetric and transitive but not reflexive (since a time
isn’t 20 minutes after itself). This is not a total ordering because some times are
incomparable to each other. For example, 1:15 is incomparable to 1:22
statisticshomeworkhelper.com
Note: This answer assumes that the question was referring to the moments in time in
a single day. Otherwise, one could argue that 1:00 precedes 1:22 on tuesday, but
1:22 on tuesday precedes 1:00 on wednesday, and then the relation would not be
antisymmetric.
(c) The relation xRy over all words in this sentence such that x does not appear after y.
(Consider “x”, “y”, and “xRy” to be words.)
Solution. T: Because all the words in the sentence are unique, this relation is transitive,
antisymmetric, and reflexive. This makes the relation a partial order. However, the
relation is also a total order, because any two elements are comparable.
(d) The relation xRy over all words in this sentence such that word x does not
appear after word y.
Solution. P: This is the same as saying that all appearances of a word x occur
before all appearances of a word y. While apparently very similar to part c, this
relation is not reflexive because the word word appears twice in the sentence. It is
transitive and antisymmetric, but not a total order because all the words between
the two occurrences of word are incomparable to word.
statisticshomeworkhelper.com
(e) The relation xRy over all words in this sentence such that the final appearance of y
occurs after x.
Solution. N: This relation is not reflexive and transitive. It is neither symmetric nor
antisymmetric; the word the occurs twice, all the others once. All words w, between the
two occurrences of the satisfy wR the and theRw. So R is not antisymmetric. But “x” is
maximal, so it’s not symmetric either
Problem 4 (35 points). To encourage collaborative study, the 6.042 staff is
considering assigning each student to a study group with two or three other
students. Prove that as long as the enrollment is large enough, the class can
always be divided into such study groups.
Solution. Proof. The proof is by strong induction. The induction hypothesis is
that a class with n ≥ 6 students can be divided into teams of 3 or 4. More
precisely
P(n) ::= n ≥ 6 −→ ∃x, y ∈ N 3x + 4y = n.
For any n ≥ 0, we may assume P(6), . . . , P(n − 1) to prove P(n).
statisticshomeworkhelper.com
Case 1: (n < 6). P(n) holds because the hypothesis n ≥ 6 is false.
Case 2: (n = 6, 7, or 8) P(6) is true because there could be two teams of 3, P(7) is
true because there could be a team of 3 and a team of 4, and P(8) is true because
there could be two teams of 4.
Case 3: (n ≥ 9). Of course n > n−3 so P(n−3) holds by the strong induction
hypothesis. But n − 3 ≥ 6, so P(n − 3) implies 3x + 4y= n − 3 for some x, y ∈ N,
and therefore 3x + 4y = n where x ::= x + 1 and y ::= y . So P(n) holds, as required.
statisticshomeworkhelper.com

More Related Content

What's hot

Unit 1 quantifiers
Unit 1  quantifiersUnit 1  quantifiers
Unit 1 quantifiers
raksharao
 
Polinomials in cd
Polinomials in cdPolinomials in cd
Polinomials in cd
Adi Sharma
 
A factorization theorem for generalized exponential polynomials with infinite...
A factorization theorem for generalized exponential polynomials with infinite...A factorization theorem for generalized exponential polynomials with infinite...
A factorization theorem for generalized exponential polynomials with infinite...Pim Piepers
 
Lecture 2 predicates quantifiers and rules of inference
Lecture 2 predicates quantifiers and rules of inferenceLecture 2 predicates quantifiers and rules of inference
Lecture 2 predicates quantifiers and rules of inferenceasimnawaz54
 
Discrete Math Lecture 02: First Order Logic
Discrete Math Lecture 02: First Order LogicDiscrete Math Lecture 02: First Order Logic
Discrete Math Lecture 02: First Order Logic
IT Engineering Department
 
Machine learning (4)
Machine learning (4)Machine learning (4)
Machine learning (4)NYversity
 
Disrete mathematics and_its application_by_rosen _7th edition_lecture_1
Disrete mathematics and_its application_by_rosen _7th edition_lecture_1Disrete mathematics and_its application_by_rosen _7th edition_lecture_1
Disrete mathematics and_its application_by_rosen _7th edition_lecture_1taimoor iftikhar
 
Advanced Microeconomics - Lecture Slides
Advanced Microeconomics - Lecture SlidesAdvanced Microeconomics - Lecture Slides
Advanced Microeconomics - Lecture Slides
Yosuke YASUDA
 
Lecture 3 qualtifed rules of inference
Lecture 3 qualtifed rules of inferenceLecture 3 qualtifed rules of inference
Lecture 3 qualtifed rules of inferenceasimnawaz54
 
Cs229 notes4
Cs229 notes4Cs229 notes4
Cs229 notes4
VuTran231
 

What's hot (12)

Unit 1 quantifiers
Unit 1  quantifiersUnit 1  quantifiers
Unit 1 quantifiers
 
Polinomials in cd
Polinomials in cdPolinomials in cd
Polinomials in cd
 
A factorization theorem for generalized exponential polynomials with infinite...
A factorization theorem for generalized exponential polynomials with infinite...A factorization theorem for generalized exponential polynomials with infinite...
A factorization theorem for generalized exponential polynomials with infinite...
 
Lecture 2 predicates quantifiers and rules of inference
Lecture 2 predicates quantifiers and rules of inferenceLecture 2 predicates quantifiers and rules of inference
Lecture 2 predicates quantifiers and rules of inference
 
Discrete Math Lecture 02: First Order Logic
Discrete Math Lecture 02: First Order LogicDiscrete Math Lecture 02: First Order Logic
Discrete Math Lecture 02: First Order Logic
 
Machine learning (4)
Machine learning (4)Machine learning (4)
Machine learning (4)
 
Disrete mathematics and_its application_by_rosen _7th edition_lecture_1
Disrete mathematics and_its application_by_rosen _7th edition_lecture_1Disrete mathematics and_its application_by_rosen _7th edition_lecture_1
Disrete mathematics and_its application_by_rosen _7th edition_lecture_1
 
Advanced Microeconomics - Lecture Slides
Advanced Microeconomics - Lecture SlidesAdvanced Microeconomics - Lecture Slides
Advanced Microeconomics - Lecture Slides
 
FUZZY LOGIC
FUZZY LOGICFUZZY LOGIC
FUZZY LOGIC
 
Lecture 3 qualtifed rules of inference
Lecture 3 qualtifed rules of inferenceLecture 3 qualtifed rules of inference
Lecture 3 qualtifed rules of inference
 
Cs229 notes4
Cs229 notes4Cs229 notes4
Cs229 notes4
 
Puy chosuantai2
Puy chosuantai2Puy chosuantai2
Puy chosuantai2
 

Similar to Stochastic Processes Homework Help

Stochastic Processes Homework Help
Stochastic Processes Homework Help Stochastic Processes Homework Help
Stochastic Processes Homework Help
Statistics Homework Helper
 
dma_ppt.pdf
dma_ppt.pdfdma_ppt.pdf
dma_ppt.pdf
BatoolKhan15
 
Math Assignment Help
Math Assignment HelpMath Assignment Help
Math Assignment Help
Math Homework Solver
 
EMBODO LP Grade 11 Anti-derivative of Polynomial Functions .docx
EMBODO LP Grade 11 Anti-derivative of Polynomial Functions .docxEMBODO LP Grade 11 Anti-derivative of Polynomial Functions .docx
EMBODO LP Grade 11 Anti-derivative of Polynomial Functions .docx
Elton John Embodo
 
Solution 3.
Solution 3.Solution 3.
Solution 3.
sansaristic
 
C2.0 propositional logic
C2.0 propositional logicC2.0 propositional logic
C2.0 propositional logic
Melaku Bayih Demessie
 
Computer Science Exam Help
Computer Science Exam Help Computer Science Exam Help
Computer Science Exam Help
Programming Exam Help
 
Signals and Systems Homework Help.pptx
Signals and Systems Homework Help.pptxSignals and Systems Homework Help.pptx
Signals and Systems Homework Help.pptx
Matlab Assignment Experts
 
Ecuaciones y Desigualdades.pdf
Ecuaciones y Desigualdades.pdfEcuaciones y Desigualdades.pdf
Ecuaciones y Desigualdades.pdf
edwinllantoy2
 
Calculus - Functions Review
Calculus - Functions ReviewCalculus - Functions Review
Calculus - Functions Reviewhassaanciit
 
1606751772-ds-lecture-6.ppt
1606751772-ds-lecture-6.ppt1606751772-ds-lecture-6.ppt
1606751772-ds-lecture-6.ppt
TejasAditya2
 
Discreate structure presentation introduction
Discreate structure presentation introductionDiscreate structure presentation introduction
Discreate structure presentation introduction
yashirraza123
 
Advance algebra
Advance algebraAdvance algebra
Advance algebra
lyra matalubos
 
1439049238 272709.Pdf
1439049238 272709.Pdf1439049238 272709.Pdf
1439049238 272709.Pdf
Andrew Parish
 
P, NP and NP-Complete, Theory of NP-Completeness V2
P, NP and NP-Complete, Theory of NP-Completeness V2P, NP and NP-Complete, Theory of NP-Completeness V2
P, NP and NP-Complete, Theory of NP-Completeness V2
S.Shayan Daneshvar
 
Algorithm Homework Help
Algorithm Homework HelpAlgorithm Homework Help
Algorithm Homework Help
Programming Homework Help
 
Algorithms Exam Help
Algorithms Exam HelpAlgorithms Exam Help
Algorithms Exam Help
Programming Exam Help
 
Predicate logic_2(Artificial Intelligence)
Predicate logic_2(Artificial Intelligence)Predicate logic_2(Artificial Intelligence)
Predicate logic_2(Artificial Intelligence)
SHUBHAM KUMAR GUPTA
 
Mathematical Statistics Assignment Help
Mathematical Statistics Assignment HelpMathematical Statistics Assignment Help
Mathematical Statistics Assignment Help
Statistics Homework Helper
 
Chi-squared Goodness of Fit Test Project Overview and.docx
Chi-squared Goodness of Fit Test Project  Overview and.docxChi-squared Goodness of Fit Test Project  Overview and.docx
Chi-squared Goodness of Fit Test Project Overview and.docx
bissacr
 

Similar to Stochastic Processes Homework Help (20)

Stochastic Processes Homework Help
Stochastic Processes Homework Help Stochastic Processes Homework Help
Stochastic Processes Homework Help
 
dma_ppt.pdf
dma_ppt.pdfdma_ppt.pdf
dma_ppt.pdf
 
Math Assignment Help
Math Assignment HelpMath Assignment Help
Math Assignment Help
 
EMBODO LP Grade 11 Anti-derivative of Polynomial Functions .docx
EMBODO LP Grade 11 Anti-derivative of Polynomial Functions .docxEMBODO LP Grade 11 Anti-derivative of Polynomial Functions .docx
EMBODO LP Grade 11 Anti-derivative of Polynomial Functions .docx
 
Solution 3.
Solution 3.Solution 3.
Solution 3.
 
C2.0 propositional logic
C2.0 propositional logicC2.0 propositional logic
C2.0 propositional logic
 
Computer Science Exam Help
Computer Science Exam Help Computer Science Exam Help
Computer Science Exam Help
 
Signals and Systems Homework Help.pptx
Signals and Systems Homework Help.pptxSignals and Systems Homework Help.pptx
Signals and Systems Homework Help.pptx
 
Ecuaciones y Desigualdades.pdf
Ecuaciones y Desigualdades.pdfEcuaciones y Desigualdades.pdf
Ecuaciones y Desigualdades.pdf
 
Calculus - Functions Review
Calculus - Functions ReviewCalculus - Functions Review
Calculus - Functions Review
 
1606751772-ds-lecture-6.ppt
1606751772-ds-lecture-6.ppt1606751772-ds-lecture-6.ppt
1606751772-ds-lecture-6.ppt
 
Discreate structure presentation introduction
Discreate structure presentation introductionDiscreate structure presentation introduction
Discreate structure presentation introduction
 
Advance algebra
Advance algebraAdvance algebra
Advance algebra
 
1439049238 272709.Pdf
1439049238 272709.Pdf1439049238 272709.Pdf
1439049238 272709.Pdf
 
P, NP and NP-Complete, Theory of NP-Completeness V2
P, NP and NP-Complete, Theory of NP-Completeness V2P, NP and NP-Complete, Theory of NP-Completeness V2
P, NP and NP-Complete, Theory of NP-Completeness V2
 
Algorithm Homework Help
Algorithm Homework HelpAlgorithm Homework Help
Algorithm Homework Help
 
Algorithms Exam Help
Algorithms Exam HelpAlgorithms Exam Help
Algorithms Exam Help
 
Predicate logic_2(Artificial Intelligence)
Predicate logic_2(Artificial Intelligence)Predicate logic_2(Artificial Intelligence)
Predicate logic_2(Artificial Intelligence)
 
Mathematical Statistics Assignment Help
Mathematical Statistics Assignment HelpMathematical Statistics Assignment Help
Mathematical Statistics Assignment Help
 
Chi-squared Goodness of Fit Test Project Overview and.docx
Chi-squared Goodness of Fit Test Project  Overview and.docxChi-squared Goodness of Fit Test Project  Overview and.docx
Chi-squared Goodness of Fit Test Project Overview and.docx
 

More from Excel Homework Help

Excel Homework Help
Excel Homework HelpExcel Homework Help
Excel Homework Help
Excel Homework Help
 
Statistics Homework Help
Statistics Homework HelpStatistics Homework Help
Statistics Homework Help
Excel Homework Help
 
Advanced Statistics Homework Help
Advanced Statistics Homework HelpAdvanced Statistics Homework Help
Advanced Statistics Homework Help
Excel Homework Help
 
Quantitative Methods Assignment Help
Quantitative Methods Assignment HelpQuantitative Methods Assignment Help
Quantitative Methods Assignment Help
Excel Homework Help
 
Data Analysis Assignment Help
Data Analysis Assignment Help Data Analysis Assignment Help
Data Analysis Assignment Help
Excel Homework Help
 
Quantitative Analysis Homework Help
Quantitative Analysis Homework HelpQuantitative Analysis Homework Help
Quantitative Analysis Homework Help
Excel Homework Help
 
Multiple Linear Regression Homework Help
Multiple Linear Regression Homework HelpMultiple Linear Regression Homework Help
Multiple Linear Regression Homework Help
Excel Homework Help
 
Mathematical Statistics Assignment Help
Mathematical Statistics Assignment HelpMathematical Statistics Assignment Help
Mathematical Statistics Assignment Help
Excel Homework Help
 
Mathematical Statistics Assignment Help
Mathematical Statistics Assignment HelpMathematical Statistics Assignment Help
Mathematical Statistics Assignment Help
Excel Homework Help
 
Mathematical Statistics Homework Help
Mathematical Statistics Homework HelpMathematical Statistics Homework Help
Mathematical Statistics Homework Help
Excel Homework Help
 
Probabilistic Systems Analysis Homework Help
Probabilistic Systems Analysis Homework HelpProbabilistic Systems Analysis Homework Help
Probabilistic Systems Analysis Homework Help
Excel Homework Help
 
Excel Homework Help
Excel Homework HelpExcel Homework Help
Excel Homework Help
Excel Homework Help
 
Probabilistic Systems Analysis Homework Help
Probabilistic Systems Analysis Homework Help Probabilistic Systems Analysis Homework Help
Probabilistic Systems Analysis Homework Help
Excel Homework Help
 
Stochastic Processes Homework Help
Stochastic Processes Homework HelpStochastic Processes Homework Help
Stochastic Processes Homework Help
Excel Homework Help
 
Stochastic Processes Homework Help
Stochastic Processes Homework HelpStochastic Processes Homework Help
Stochastic Processes Homework Help
Excel Homework Help
 

More from Excel Homework Help (15)

Excel Homework Help
Excel Homework HelpExcel Homework Help
Excel Homework Help
 
Statistics Homework Help
Statistics Homework HelpStatistics Homework Help
Statistics Homework Help
 
Advanced Statistics Homework Help
Advanced Statistics Homework HelpAdvanced Statistics Homework Help
Advanced Statistics Homework Help
 
Quantitative Methods Assignment Help
Quantitative Methods Assignment HelpQuantitative Methods Assignment Help
Quantitative Methods Assignment Help
 
Data Analysis Assignment Help
Data Analysis Assignment Help Data Analysis Assignment Help
Data Analysis Assignment Help
 
Quantitative Analysis Homework Help
Quantitative Analysis Homework HelpQuantitative Analysis Homework Help
Quantitative Analysis Homework Help
 
Multiple Linear Regression Homework Help
Multiple Linear Regression Homework HelpMultiple Linear Regression Homework Help
Multiple Linear Regression Homework Help
 
Mathematical Statistics Assignment Help
Mathematical Statistics Assignment HelpMathematical Statistics Assignment Help
Mathematical Statistics Assignment Help
 
Mathematical Statistics Assignment Help
Mathematical Statistics Assignment HelpMathematical Statistics Assignment Help
Mathematical Statistics Assignment Help
 
Mathematical Statistics Homework Help
Mathematical Statistics Homework HelpMathematical Statistics Homework Help
Mathematical Statistics Homework Help
 
Probabilistic Systems Analysis Homework Help
Probabilistic Systems Analysis Homework HelpProbabilistic Systems Analysis Homework Help
Probabilistic Systems Analysis Homework Help
 
Excel Homework Help
Excel Homework HelpExcel Homework Help
Excel Homework Help
 
Probabilistic Systems Analysis Homework Help
Probabilistic Systems Analysis Homework Help Probabilistic Systems Analysis Homework Help
Probabilistic Systems Analysis Homework Help
 
Stochastic Processes Homework Help
Stochastic Processes Homework HelpStochastic Processes Homework Help
Stochastic Processes Homework Help
 
Stochastic Processes Homework Help
Stochastic Processes Homework HelpStochastic Processes Homework Help
Stochastic Processes Homework Help
 

Recently uploaded

The geography of Taylor Swift - some ideas
The geography of Taylor Swift - some ideasThe geography of Taylor Swift - some ideas
The geography of Taylor Swift - some ideas
GeoBlogs
 
Sha'Carri Richardson Presentation 202345
Sha'Carri Richardson Presentation 202345Sha'Carri Richardson Presentation 202345
Sha'Carri Richardson Presentation 202345
beazzy04
 
Additional Benefits for Employee Website.pdf
Additional Benefits for Employee Website.pdfAdditional Benefits for Employee Website.pdf
Additional Benefits for Employee Website.pdf
joachimlavalley1
 
Adversarial Attention Modeling for Multi-dimensional Emotion Regression.pdf
Adversarial Attention Modeling for Multi-dimensional Emotion Regression.pdfAdversarial Attention Modeling for Multi-dimensional Emotion Regression.pdf
Adversarial Attention Modeling for Multi-dimensional Emotion Regression.pdf
Po-Chuan Chen
 
Language Across the Curriculm LAC B.Ed.
Language Across the  Curriculm LAC B.Ed.Language Across the  Curriculm LAC B.Ed.
Language Across the Curriculm LAC B.Ed.
Atul Kumar Singh
 
Lapbook sobre os Regimes Totalitários.pdf
Lapbook sobre os Regimes Totalitários.pdfLapbook sobre os Regimes Totalitários.pdf
Lapbook sobre os Regimes Totalitários.pdf
Jean Carlos Nunes Paixão
 
Mule 4.6 & Java 17 Upgrade | MuleSoft Mysore Meetup #46
Mule 4.6 & Java 17 Upgrade | MuleSoft Mysore Meetup #46Mule 4.6 & Java 17 Upgrade | MuleSoft Mysore Meetup #46
Mule 4.6 & Java 17 Upgrade | MuleSoft Mysore Meetup #46
MysoreMuleSoftMeetup
 
Polish students' mobility in the Czech Republic
Polish students' mobility in the Czech RepublicPolish students' mobility in the Czech Republic
Polish students' mobility in the Czech Republic
Anna Sz.
 
Overview on Edible Vaccine: Pros & Cons with Mechanism
Overview on Edible Vaccine: Pros & Cons with MechanismOverview on Edible Vaccine: Pros & Cons with Mechanism
Overview on Edible Vaccine: Pros & Cons with Mechanism
DeeptiGupta154
 
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...
Levi Shapiro
 
Chapter 3 - Islamic Banking Products and Services.pptx
Chapter 3 - Islamic Banking Products and Services.pptxChapter 3 - Islamic Banking Products and Services.pptx
Chapter 3 - Islamic Banking Products and Services.pptx
Mohd Adib Abd Muin, Senior Lecturer at Universiti Utara Malaysia
 
The approach at University of Liverpool.pptx
The approach at University of Liverpool.pptxThe approach at University of Liverpool.pptx
The approach at University of Liverpool.pptx
Jisc
 
How libraries can support authors with open access requirements for UKRI fund...
How libraries can support authors with open access requirements for UKRI fund...How libraries can support authors with open access requirements for UKRI fund...
How libraries can support authors with open access requirements for UKRI fund...
Jisc
 
Instructions for Submissions thorugh G- Classroom.pptx
Instructions for Submissions thorugh G- Classroom.pptxInstructions for Submissions thorugh G- Classroom.pptx
Instructions for Submissions thorugh G- Classroom.pptx
Jheel Barad
 
Model Attribute Check Company Auto Property
Model Attribute  Check Company Auto PropertyModel Attribute  Check Company Auto Property
Model Attribute Check Company Auto Property
Celine George
 
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdf
Welcome to TechSoup   New Member Orientation and Q&A (May 2024).pdfWelcome to TechSoup   New Member Orientation and Q&A (May 2024).pdf
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdf
TechSoup
 
678020731-Sumas-y-Restas-Para-Colorear.pdf
678020731-Sumas-y-Restas-Para-Colorear.pdf678020731-Sumas-y-Restas-Para-Colorear.pdf
678020731-Sumas-y-Restas-Para-Colorear.pdf
CarlosHernanMontoyab2
 
Honest Reviews of Tim Han LMA Course Program.pptx
Honest Reviews of Tim Han LMA Course Program.pptxHonest Reviews of Tim Han LMA Course Program.pptx
Honest Reviews of Tim Han LMA Course Program.pptx
timhan337
 
A Strategic Approach: GenAI in Education
A Strategic Approach: GenAI in EducationA Strategic Approach: GenAI in Education
A Strategic Approach: GenAI in Education
Peter Windle
 
Unit 2- Research Aptitude (UGC NET Paper I).pdf
Unit 2- Research Aptitude (UGC NET Paper I).pdfUnit 2- Research Aptitude (UGC NET Paper I).pdf
Unit 2- Research Aptitude (UGC NET Paper I).pdf
Thiyagu K
 

Recently uploaded (20)

The geography of Taylor Swift - some ideas
The geography of Taylor Swift - some ideasThe geography of Taylor Swift - some ideas
The geography of Taylor Swift - some ideas
 
Sha'Carri Richardson Presentation 202345
Sha'Carri Richardson Presentation 202345Sha'Carri Richardson Presentation 202345
Sha'Carri Richardson Presentation 202345
 
Additional Benefits for Employee Website.pdf
Additional Benefits for Employee Website.pdfAdditional Benefits for Employee Website.pdf
Additional Benefits for Employee Website.pdf
 
Adversarial Attention Modeling for Multi-dimensional Emotion Regression.pdf
Adversarial Attention Modeling for Multi-dimensional Emotion Regression.pdfAdversarial Attention Modeling for Multi-dimensional Emotion Regression.pdf
Adversarial Attention Modeling for Multi-dimensional Emotion Regression.pdf
 
Language Across the Curriculm LAC B.Ed.
Language Across the  Curriculm LAC B.Ed.Language Across the  Curriculm LAC B.Ed.
Language Across the Curriculm LAC B.Ed.
 
Lapbook sobre os Regimes Totalitários.pdf
Lapbook sobre os Regimes Totalitários.pdfLapbook sobre os Regimes Totalitários.pdf
Lapbook sobre os Regimes Totalitários.pdf
 
Mule 4.6 & Java 17 Upgrade | MuleSoft Mysore Meetup #46
Mule 4.6 & Java 17 Upgrade | MuleSoft Mysore Meetup #46Mule 4.6 & Java 17 Upgrade | MuleSoft Mysore Meetup #46
Mule 4.6 & Java 17 Upgrade | MuleSoft Mysore Meetup #46
 
Polish students' mobility in the Czech Republic
Polish students' mobility in the Czech RepublicPolish students' mobility in the Czech Republic
Polish students' mobility in the Czech Republic
 
Overview on Edible Vaccine: Pros & Cons with Mechanism
Overview on Edible Vaccine: Pros & Cons with MechanismOverview on Edible Vaccine: Pros & Cons with Mechanism
Overview on Edible Vaccine: Pros & Cons with Mechanism
 
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...
 
Chapter 3 - Islamic Banking Products and Services.pptx
Chapter 3 - Islamic Banking Products and Services.pptxChapter 3 - Islamic Banking Products and Services.pptx
Chapter 3 - Islamic Banking Products and Services.pptx
 
The approach at University of Liverpool.pptx
The approach at University of Liverpool.pptxThe approach at University of Liverpool.pptx
The approach at University of Liverpool.pptx
 
How libraries can support authors with open access requirements for UKRI fund...
How libraries can support authors with open access requirements for UKRI fund...How libraries can support authors with open access requirements for UKRI fund...
How libraries can support authors with open access requirements for UKRI fund...
 
Instructions for Submissions thorugh G- Classroom.pptx
Instructions for Submissions thorugh G- Classroom.pptxInstructions for Submissions thorugh G- Classroom.pptx
Instructions for Submissions thorugh G- Classroom.pptx
 
Model Attribute Check Company Auto Property
Model Attribute  Check Company Auto PropertyModel Attribute  Check Company Auto Property
Model Attribute Check Company Auto Property
 
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdf
Welcome to TechSoup   New Member Orientation and Q&A (May 2024).pdfWelcome to TechSoup   New Member Orientation and Q&A (May 2024).pdf
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdf
 
678020731-Sumas-y-Restas-Para-Colorear.pdf
678020731-Sumas-y-Restas-Para-Colorear.pdf678020731-Sumas-y-Restas-Para-Colorear.pdf
678020731-Sumas-y-Restas-Para-Colorear.pdf
 
Honest Reviews of Tim Han LMA Course Program.pptx
Honest Reviews of Tim Han LMA Course Program.pptxHonest Reviews of Tim Han LMA Course Program.pptx
Honest Reviews of Tim Han LMA Course Program.pptx
 
A Strategic Approach: GenAI in Education
A Strategic Approach: GenAI in EducationA Strategic Approach: GenAI in Education
A Strategic Approach: GenAI in Education
 
Unit 2- Research Aptitude (UGC NET Paper I).pdf
Unit 2- Research Aptitude (UGC NET Paper I).pdfUnit 2- Research Aptitude (UGC NET Paper I).pdf
Unit 2- Research Aptitude (UGC NET Paper I).pdf
 

Stochastic Processes Homework Help

  • 1. Discrete Stochastic Processes statisticshomeworkhelper.com For any help regarding Stochastic Processes Homework Help Visit :- https://www.excelhomeworkhelp.com/, Email :- info@excelhomeworkhelp.com or Call us at :- +1 678 648 4277
  • 2. Circle the name of your Tutorial Instructor: Ashish Carole Christos Eric George Jack Nick Tina • This quiz is closed book. There is an Appendix giving the definitions of standard properties of relations. • There are four (4) problems totaling 100 points. Problems are labeled with their point values. • Put your name on the top of every page – these pages may be separated for grading. • Write your solutions in the space provided. If you need more space, write on the back of the sheet containing the problem. Please keep your entire answer to a problem on that problem’s page. • You may assume any of the results presented in class or in the lecture notes. • Be neat and write legibly. You will be graded not only on the correctness of your answer, but also on the clarity with which you express it. Problems statisticshomeworkhelper.com
  • 3. Problem 1 Consider the following system specifications1. 1. The system is in multiuser state if it is operating normally. 2. If the system is operating normally, then the kernel is functioning. 3. The kernel is not functioning or the system is in interrupt mode. 4. If the system is not in multiuser state, then it is in interrupt mode. 5. The system is not in interrupt mode. (a) To make sense of these confusing conditions, let’s introduce four Boolean variables. statisticshomeworkhelper.com
  • 4. M ::= in Multiuser state (1) N ::= operating Normally (2) K ::= Kernel is functioning (3) I ::= in Interrupt mode (4) Translate the five statements in the specification into propositional logic notation: ∧,∨,¬, −→ statisticshomeworkhelper.com
  • 5. (b) Are these system specifications consistent? . Prove it! Problem 2 For each of the following logical formulas with domain of discourse the natural numbers, N, indicate whether it is a possible formulation of I: the Induction Axiom, S: the Strong Induction Axiom, L: the Least Number Principle (also known as Wellordering), or N: None of these. For example, the ordinary Induction Axiom could be expressed by the following formula, so it gets labelled “I”. statisticshomeworkhelper.com
  • 6. This is a multiple choice problem: do not explain your answer. (a) (P(b) ∧ [∀k ≥ b P(k) −→ P(k + 1)]) −→ ∀k ≥ b P(k) (b) (P(b) ∧ [∀k ≤ b P(k) −→ P(k + 1)]) −→ ∀k ≤ b P(k) (c) [∀b (∀k < b P(k)) −→ P(b)] −→ ∀k P(k) ( (d) (∃n P(n)) −→ ∃n ∀k < n P(k) (e) ∀n [P(n) −→ (∃n P(n) ∧ ∀k < n P(k))] Problem 3 Classify each of the following binary relations as E: An equivalence relation. T: A Total order, P: A Partial order that is not total. S: A Symmetric relation that is not transitive. N: None of the above.. statisticshomeworkhelper.com
  • 7. This is a multiple choice problem: do not explain your answer. (a) The relation xRy between times of day such that x and y are at most twenty minutes apart. (b) The relation xRy between times of day such that x is more than twenty minutes later than y. (c) The relation xRy over all words in this sentence such that x does not appear after y. (Consider “x”, “y”, and “xRy” to be words.) (d) The relation xRy over all words in this sentence such that word x does not appear after word y. (e) The relation xRy over all words in this sentence such that the final appearance of y occurs after x. Problem 4 To encourage collaborative study, the 6.042 staff is considering assigning each student to a study group with two or three other students. Prove that as long as the enrollment is large enough, the class can always be divided into such study groups. statisticshomeworkhelper.com
  • 8. Appendix A binary relation, R, on a set A is • reflexive iff xRx, • symmetric iff xRy −→ yRx, • antisymmetric iff xRy ∧ yRx −→ x = y, • transitive iff xRy ∧ yRz −→ xRz, for all x, y, z ∈ A. • R is an equivalence relation iff it is reflexive, symmetric and transitive. • R is a partial order iff it is transitive and antisymmetric. • R is a total order iff it is a partial order and for all x = y ∈ A either xRy or yRx. statisticshomeworkhelper.com
  • 9. Problem 1 Consider the following system specifications1. 1. The system is in multiuser state iff it is operating normally. 2. If the system is operating normally, then the kernel is functioning. 3. The kernel is not functioning or the system is in interrupt mode. 4. If the system is not in multiuser state, then it is in interrupt mode. 5. The system is not in interrupt mode. (a) To make sense of these confusing conditions, let’s introduce four Boolean variables. M ::= in Multiuser state (1) N ::= operating Normally (2) K ::= Kernel is functioning (3) I ::= in Interrupt mode (4) statisticshomeworkhelper.com
  • 10. Translate the five statements in the specification into propositional logic notation: ∧,∨,¬, −→ ,←→ Solution. M N ←→ (5) N −→ K (6) ¬K ∨ I (7) ¬M −→ I (8) ¬I (9) (b) (0 points) Are these system specifications consistent? . Prove it! Solution. There are several ways to approach this problem. One is to construct a truth table with sixteen lines—one for each way of assigning truth values to the four variables M, N, K, and I. We can avoid the cumbersome truthtable if we reason by cases. Case 1: I is true. Then the last formula (9) is false, and the whole specification is false. Case 2: I is false. Now formula (8) can be true only if ¬M is false, that is, only if M is true. Likewise, formula (7) can be true only if ¬K is true, that is, K is false. Since K is false, formula (6) can be true only if N is false. Thus, we have deduced that in order to be consistent in this case, we must have statisticshomeworkhelper.com
  • 11. I = false M = true K = false N = false. But now formula (5) is false, so it is impossible for all the formulas to be true: the system is inconsistent. Problem 2 For each of the following logical formulas with domain of discourse the natural numbers, N, indicate whether it is a possible formulation of I: the Induction Axiom, S: the Strong Induction Axiom, L: the Least Number Principle (also known as Wellordering), or N: None of these. For example, the ordinary Induction Axiom could be expressed by the following formula, so it gets labelled “I”. (P(0) ∧ [∀k P(k) −→ P(k + 1)]) −→ ∀k P(k) I statisticshomeworkhelper.com
  • 12. This is a multiple choice problem: do not explain your answer. (a) (P(b) ∧ [∀k ≥ b P(k) −→ P(k + 1)]) −→ ∀k ≥ b P(k) Solution. I. This is a perfect formulation of the Induction Axiom. b is used for the base case; P(k) −→ P(k + 1) is the inductive case. (b) (P(b) ∧ [∀k ≤ b P(k) −→ P(k + 1)]) −→ ∀k ≤ b P(k) Solution. N. The two occurences of k <= b should have been k >= b (c) [∀b (∀k < b P(k)) −→ P(b)] −→ ∀k P(k) Solution. S. Since you are assuming P(k) for all k < b, this is strong induction. (d) (∃n P(n)) −→ ∃n ∀k < n P(k) Solution. N. This statement is in fact always true; when n = 0, ∀k < n P(k). It should say P(n) ∧ ∀k < n; P(k) (e) ∀n [P(n) −→ (∃n P(n) ∧ ∀k < n P(k))] Solution. L. This is a valid formulation; it does not have that problem in (2d). Problem 3 . Classify each of the following binary relations as statisticshomeworkhelper.com
  • 13. E: An equivalence relation. T: A Total order, P: A Partial order that is not total S: A Symmetric relation that is not transitive. N: None of the above. This is a multiple choice problem: do not explain your answer. (a) The relation xRy between times of day such that x and y are at most twenty minutes apart. Solution. S: This relation is reflexive and symmetric. It is not transitive; Consider the counterexample: 1:00R 1:15, 1:15R 1:22 but ¬1:00R 1:22. (b) The relation xRy between times of day such that x is more than twenty minutes later than y. Solution. P: This relation is antisymmetric and transitive but not reflexive (since a time isn’t 20 minutes after itself). This is not a total ordering because some times are incomparable to each other. For example, 1:15 is incomparable to 1:22 statisticshomeworkhelper.com
  • 14. Note: This answer assumes that the question was referring to the moments in time in a single day. Otherwise, one could argue that 1:00 precedes 1:22 on tuesday, but 1:22 on tuesday precedes 1:00 on wednesday, and then the relation would not be antisymmetric. (c) The relation xRy over all words in this sentence such that x does not appear after y. (Consider “x”, “y”, and “xRy” to be words.) Solution. T: Because all the words in the sentence are unique, this relation is transitive, antisymmetric, and reflexive. This makes the relation a partial order. However, the relation is also a total order, because any two elements are comparable. (d) The relation xRy over all words in this sentence such that word x does not appear after word y. Solution. P: This is the same as saying that all appearances of a word x occur before all appearances of a word y. While apparently very similar to part c, this relation is not reflexive because the word word appears twice in the sentence. It is transitive and antisymmetric, but not a total order because all the words between the two occurrences of word are incomparable to word. statisticshomeworkhelper.com
  • 15. (e) The relation xRy over all words in this sentence such that the final appearance of y occurs after x. Solution. N: This relation is not reflexive and transitive. It is neither symmetric nor antisymmetric; the word the occurs twice, all the others once. All words w, between the two occurrences of the satisfy wR the and theRw. So R is not antisymmetric. But “x” is maximal, so it’s not symmetric either Problem 4 (35 points). To encourage collaborative study, the 6.042 staff is considering assigning each student to a study group with two or three other students. Prove that as long as the enrollment is large enough, the class can always be divided into such study groups. Solution. Proof. The proof is by strong induction. The induction hypothesis is that a class with n ≥ 6 students can be divided into teams of 3 or 4. More precisely P(n) ::= n ≥ 6 −→ ∃x, y ∈ N 3x + 4y = n. For any n ≥ 0, we may assume P(6), . . . , P(n − 1) to prove P(n). statisticshomeworkhelper.com
  • 16. Case 1: (n < 6). P(n) holds because the hypothesis n ≥ 6 is false. Case 2: (n = 6, 7, or 8) P(6) is true because there could be two teams of 3, P(7) is true because there could be a team of 3 and a team of 4, and P(8) is true because there could be two teams of 4. Case 3: (n ≥ 9). Of course n > n−3 so P(n−3) holds by the strong induction hypothesis. But n − 3 ≥ 6, so P(n − 3) implies 3x + 4y= n − 3 for some x, y ∈ N, and therefore 3x + 4y = n where x ::= x + 1 and y ::= y . So P(n) holds, as required. statisticshomeworkhelper.com