Hello,
This is Tahsin Ahmed Nasim. I'm a student of Civil Engineering. My Own MARKOV CHAINS Presentation.
This is the part of Probability of Statistic.
This ppt includes the definition of the Markov process, Markov chain. Some real-life examples and applications. It also includes some of its advantages and limitations.
Markov chains are a very common model for systems that change probablistically over time. We show a few fun examples, define the objects, state the main theorems, and show how to find the steady-state vector.
Why should you care about Markov Chain Monte Carlo methods?
→ They are in the list of "Top 10 Algorithms of 20th Century"
→ They allow you to make inference with Bayesian Networks
→ They are used everywhere in Machine Learning and Statistics
Markov Chain Monte Carlo methods are a class of algorithms used to sample from complicated distributions. Typically, this is the case of posterior distributions in Bayesian Networks (Belief Networks).
These slides cover the following topics.
→ Motivation and Practical Examples (Bayesian Networks)
→ Basic Principles of MCMC
→ Gibbs Sampling
→ Metropolis–Hastings
→ Hamiltonian Monte Carlo
→ Reversible-Jump Markov Chain Monte Carlo
Introduction To Markov Chains | Markov Chains in Python | EdurekaEdureka!
YouTube Link: https://youtu.be/Gs2xtNzogSY
** Python Data Science Training: https://www.edureka.co/data-science-python-certification-course **
This Edureka session on Introduction To Markov Chains will help you understand the basic idea behind Markov chains and how they can be modeled as a solution to real-world problems.
Here’s a list of topics that will be covered in this session:
1. What Is A Markov Chain?
2. What Is The Markov Property?
3. Understanding Markov Chains With An Example
4. What Is A Transition Matrix?
5. Markov Chain In Python
6. Markov Chain Applications
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Knowledge representation and reasoning (KR) is the field of artificial intelligence (AI) dedicated to representing information about the world in a form that a computer system can utilize to solve complex tasks such as diagnosing a medical condition or having a dialog in a natural language
Hello,
This is Tahsin Ahmed Nasim. I'm a student of Civil Engineering. My Own MARKOV CHAINS Presentation.
This is the part of Probability of Statistic.
This ppt includes the definition of the Markov process, Markov chain. Some real-life examples and applications. It also includes some of its advantages and limitations.
Markov chains are a very common model for systems that change probablistically over time. We show a few fun examples, define the objects, state the main theorems, and show how to find the steady-state vector.
Why should you care about Markov Chain Monte Carlo methods?
→ They are in the list of "Top 10 Algorithms of 20th Century"
→ They allow you to make inference with Bayesian Networks
→ They are used everywhere in Machine Learning and Statistics
Markov Chain Monte Carlo methods are a class of algorithms used to sample from complicated distributions. Typically, this is the case of posterior distributions in Bayesian Networks (Belief Networks).
These slides cover the following topics.
→ Motivation and Practical Examples (Bayesian Networks)
→ Basic Principles of MCMC
→ Gibbs Sampling
→ Metropolis–Hastings
→ Hamiltonian Monte Carlo
→ Reversible-Jump Markov Chain Monte Carlo
Introduction To Markov Chains | Markov Chains in Python | EdurekaEdureka!
YouTube Link: https://youtu.be/Gs2xtNzogSY
** Python Data Science Training: https://www.edureka.co/data-science-python-certification-course **
This Edureka session on Introduction To Markov Chains will help you understand the basic idea behind Markov chains and how they can be modeled as a solution to real-world problems.
Here’s a list of topics that will be covered in this session:
1. What Is A Markov Chain?
2. What Is The Markov Property?
3. Understanding Markov Chains With An Example
4. What Is A Transition Matrix?
5. Markov Chain In Python
6. Markov Chain Applications
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
Knowledge representation and reasoning (KR) is the field of artificial intelligence (AI) dedicated to representing information about the world in a form that a computer system can utilize to solve complex tasks such as diagnosing a medical condition or having a dialog in a natural language
Markov Chains for the Web - SEO, Usability, Search Engine Scoring, and MoreRivka Fogel
Markov chains can take predictive theory to a new level, with large-scale applications for digital marketing. From social media network modeling to user pathing, site scoring and recommended pages, Markov chains can quantify, rank, and return likely outcomes on the web. In other words, they can demystify demographics. Here's how.
Elephant and the mice (Powerpoint Presentation)Muktalal
This powerpoint presentation displays the traditional story identified by the students of S.D.P.School, Pitampura, Delhi, India that explores the scientific principle behind it.
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International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
Dynamic asset allocation under regime switching: an in-sample and out-of-samp...Andrea Bartolucci
My work consists of a comparative study of the performances of the multivariate regime switching model against the single regime model in terms of portfolio returns in the context of dynamic asset allocation.
The study was conducted through the practical application, both in-sample and out of-sample, of the two models under various portfolio optimization approaches.
In the first part of the asset allocation exercise I constructed for any asset pricing model, both in-sample and out-of-sample, two dynamic recursive efficient portfolios that maximize the Sharpe among portfolios on the efficient frontier (one with opened budget constraint that permits between 0% and 100% in the riskless asset, one whose weights must sum to 1); in addition short selling, thus negative asset class weights, is not allowed. The other three dynamic recursive portfolios that I constructed have been chosen as those that maximize the investor utility function with three different risk aversion coefficient subject to non-negative weights and opened upper budget constraint.
The second part of the asset allocation exercise focuses only on the out-of-sample period. Here the Copula-Opinion Pooling approach is applied to implement in the asset pricing model views on the asset returns produced by both the single regime model and the regime switching model. The purpose of this section is to investigate and make a comparison of the behavior of the regime switching model and the single state model in the COP framework in terms of both expected and realized portfolio returns and Sharpe ratio in the context of mean-variance and conditional value-atrisk (CVaR) portfolio optimization. Therefore, in addition to the five recursive optimal portfolios chosen with the same portfolio selection process as in the first part, here using conditional value-at-risk as the risk exposure constraint, I derived the dynamic optimal weights of other five different portfolios equally distributed, in terms of CVaR, along the time dependent efficient frontier for different values of the confidence in the views.
The overperformance can be achieved by the more efficient and desirable risk-reward combinations on the state-dependent frontier that can be obtained only by systematically altering portfolio allocations in response to changes in the investment opportunities as the economy switches back and forth among different states. An investor who ignores regimes sits on the unconditional frontier, thus an investor can do better by holding a higher Sharpe ratio portfolio when the low volatility regime prevails. Conversely, when the bad regime occurs, the investor who ignores regimes holds too high a risky asset weight. She would have been better off shifting into the risk-free asset when the bear regime hits. As a consequence, the presence of two regimes and two frontiers means that the regime switching investment opportunity set dominates the investment opportunity set offered by one frontier.
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Send your semester & Specialization name to our mail id :
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or
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Research on product quality control of multi varieties and small batch based ...IRJESJOURNAL
ABSTRACT: -This paper mainly studies the application of statistical process control in multi-variety and small-batch production environment. The paper puts forward the method of quality control based on Bayesian theory. First, Bayesian theory is used to estimate the parameters of the production process. Then Bayesian model is used to control the production of many varieties and small batches based on Bayesian parameter estimation. A Bayesian control model identification method is proposed. Finally, an example is given to verify the feasibility of the method. The results show that this method can be a quality control method for many kinds of small batch products
Water pollution affects plants and organisms living in these bodies of water; and, in almost all cases the effect is damaging either to individual species and populations, but also to the natural biological communities.
Natural resources (economically referred to as land or raw materials) occur naturally within environments that exist relatively undisturbed by mankind, in a natural form. A natural resource is often characterized by amounts of biodiversity existent in various ecosystems.
An ecosystem is generally an area within the natural environment in which physical (abiotic) factors of the environment, such as rocks and soil, function together along with interdependent (biotic) organisms, such as plants and animals, within the same habitat
What Does Sensitivity Analysis Mean?
A technique used to determine how different values of an independent variable will impact a particular dependent variable under a given set of assumptions. This technique is used within specific boundaries that will depend on one or more input variables, such as the effect that changes in interest rates will have on a bond's price.
Sensitivity analysis is a way to predict the outcome of a decision if a situation turns out to be different compared to the key prediction(s).
matrices
The beginnings of matrices goes back to the second century BC although traces can be seen back to the fourth century BC. However it was not until near the end of the 17th Century that the ideas reappeared and development really got underway.
It is not surprising that the beginnings of matrices and determinants should arise through the study of systems of linear equations. The Babylonians studied problems which lead to simultaneous linear equations and some of these are preserved in clay tablets which survive.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Neuro-symbolic is not enough, we need neuro-*semantic*
M A R K O V C H A I N
1. A SEMINAR ON MARKOV CHAIN AND PROCESS PRESENTED BY PANKAJ A. DEOKATE SAGAR TIKKAS GUIDED BY MR.ASHISH GHORPADE tqma2z.blogspot.com SHRI SHIVAJI SHIKSHAN SANSTHA DR. PANJBRAO DESHMUKH INST. OF MANAGEMENT TECHNOLOGY& RESEARCH,NAGPUR
2.
3.
4. D1 D2 D3 D1 D2 D3 Brand switching example BRAND THIS MONTH BRAND NEXT MONTH In the given problem ,the aim is to determine the behaviour of the system. the situation is dynamic in sense that it involve multiple period (differnt time period ) requiring to customer to make sequence of decision in chance environment ,involving two or more possible outcome at fixed interval of time .In short given process may be defined in stochastic process. Discrete time stochastic process : let Xt be the value of system characteristic at time t, and it may be vive as random variable a description of a relation between random variable at X0,X1,X2 and so on called Discrete time stochastic process transition of switching from one brand to another brand is called transition probality.
5. 3.Markov process it includes 1.finite states - absorbing states in regards to the classification of states,it may be further noted that, a. for two states i,j, a sequence of transition thats begins in i and ends in j is called " path " from i to j. b. A states j is known as "reachable" from state i if there is path from i to j c. communicative d. transient 2.first order process in this context,it may be mention that where the probability of the next event depends upon the outcome of the last event like customer choice of brands in given month is depends upon choice in the last month,the markov process is terms as first order markov process, similarly second order mar. process
6. 3.Stationarity: Transition probability are constant over the time. 4.Uniform time period: The changes from one state to another takes place only ones during each time period and time period are equal in duration.
7. MARKOV ANALYSIS: INPUT AND OUTPUT In the Markov analysis ,the analysis of given system is based on the following two sets of inputs data 1.Transition matrix (containing transition probability) 2.The initial condition in which the system is based on these inputs problem 01: A market survey is made on two brands of breakfast food A and B. every times a customer purchase, he may buy a same brand or switch to another brands the transition matrix is given bellow, at present its estimated that 60% of the people buy brand A and 40% buy brand B. determine the market share of brand A and B in stedy state. TO FROM A B A 0.8 0.2 B 0.6 0.4
8. Application 1.The application of Markov chain IN CHEMICAL ENGINEERING 2. The application of Markov chain analysis to oligonucleotide frequency prediction 3. The application of Markov chain in marketing as a brand product prediction
9.
10. References [1] Business Statistics, G. C. Beri (TMH) [2] Quantitative Techniques in Management, N. D. Vohra (TMH) [3] Quantitative Methods For Business, Anderson ( Thomson Learning Books) [4] Statistical methods, S.P. Gupta ( S Chand) [5] Levin Richard & Rubin David - Statistics for Management (Prentice Hall of India)