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Q5: Find the most cost-effective path to reach from Node A to Node J using A* Algorithm

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Problem Statement Use the A* (A-Star) algorithm to find the most cost-effective path from Node A to Node J in the given graph. Edge weights represent the cost (distance), and node values represent the heuristic (h(n)) to the goal node (J). Given Graph Structure: Nodes and their heuristics (h): A (10), B (3), C (2), […]

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Compare Artificial Intelligence, Machine Learning, and Deep Learning

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Introduction Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are closely related terms but they are not the same. Each concept builds upon the previous one, with AI being the broadest, ML being a subset of AI, and DL being a further subset of ML. Understanding the differences and relationships among these terms

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Define Supervised, Unsupervised and Reinforcement learning with a suitable examples of each

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Introduction Machine Learning (ML) is a subset of Artificial Intelligence (AI) that enables machines to learn from data and improve their performance without being explicitly programmed. ML techniques can be classified into three main types: Supervised Learning, Unsupervised Learning, and Reinforcement Learning. Each type of learning solves different types of problems and uses distinct methods

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Classify AI on the basis of the functionalities of AI. Also discuss some important applications of AI.

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Introduction Artificial Intelligence (AI) is a multidisciplinary field that aims to create machines capable of performing tasks that typically require human intelligence. These tasks include reasoning, learning, problem-solving, understanding natural language, perception, and even physical movement. AI is rapidly evolving and is classified based on its functionalities and capabilities. In this answer, we will focus

Classify AI on the basis of the functionalities of AI. Also discuss some important applications of AI. Read More »

MECE-102: Advanced Econometric Methods – Assignment Answer Key 2024-25

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MECE-102: ADVANCED ECONOMETRIC METHODS Tutor Marked Assignment Course Code: MECE-102 Asst. Code: MECE-102/AST/2024-25 Maximum Marks: 100 Note: Answer all the questions. While questions in Section A carry 20 marks each, those in Section B carry 12 marks each. Section A a) What is simultaneity bias? Explain the conditions required for identification of parameters in a

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Write short notes on the following: a) ARCH model b) Granger-causality

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a) ARCH Model (Autoregressive Conditional Heteroskedasticity) The ARCH model, introduced by Robert Engle in 1982, is used to model and forecast time-varying volatility in time series data, especially in financial markets where periods of high and low volatility alternate. Key Features: Conditional heteroskedasticity: The variance of the error term depends on past squared errors. Captures

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What are the advantages of panel data models? Specify the fixed effects model and explain how it can be estimated.

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Introduction Panel data models have gained significant importance in empirical economic research due to their ability to control for unobserved heterogeneity and improve the reliability of estimates. Panel data refers to multi-dimensional data involving observations over time for the same individuals, households, firms, or countries. Advantages of Panel Data Models Controls for Unobserved Heterogeneity: Panel

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Explain the central idea behind the multinomial logit model. What the underlying assumptions in this model?

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Introduction The Multinomial Logit Model (MNL) is an extension of the binary logit model used in econometrics when the dependent variable has more than two unordered categories. It is frequently applied in modeling individual choices among multiple discrete alternatives, such as choosing between different brands, modes of transportation, or political parties. Central Idea Behind the

Explain the central idea behind the multinomial logit model. What the underlying assumptions in this model? Read More »

What is meant by dynamic model? Explain how the following model can be estimated? 𝑦𝑡 =∝ +𝛽𝑥𝑡 + 𝛾𝑦𝑡−1 + 𝑢𝑡 where |𝛾| < 1 and 𝑢𝑡 = 𝜌 𝑢𝑡−1+ 𝜀𝑡

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Introduction In econometrics, a dynamic model is one that includes lagged values of the dependent or independent variables. These models are particularly useful when analyzing time series data where past events influence current outcomes. Dynamic models are essential for studying the adjustment process and persistence over time. What is a Dynamic Model? A dynamic model

What is meant by dynamic model? Explain how the following model can be estimated? 𝑦𝑡 =∝ +𝛽𝑥𝑡 + 𝛾𝑦𝑡−1 + 𝑢𝑡 where |𝛾| < 1 and 𝑢𝑡 = 𝜌 𝑢𝑡−1+ 𝜀𝑡 Read More »

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