What are Probabilistic Graphical Models?

Probabilistic Graphical Models (PGMs) are frameworks that represent complex distributions over random variables using graphs. They integrate probability theory and graphical representations, which can simplify the analysis of uncertain domains. By capturing dependencies and independencies between variables, PGMs can efficiently model and infer relationships within data. This makes them crucial in fields like machine learning, computer vision, and natural language processing. For example, a PGM could be used to predict outcomes in a medical diagnosis task by integrating various symptoms and test results.

How do PGMs differ from traditional statistical models?

Probabilistic Graphical Models differ from traditional models primarily in their ability to represent high-dimensional probability distributions through graphical structures. While traditional statistical models often assume independence among variables, PGMs allow for the representation of dependencies and interactions, making them more suitable for complex datasets. For instance, in a network of social interactions, a PGM can depict how friends influence each other’s behavior, whereas a traditional model might treat each individual’s behavior independently.

What are the main components of Probabilistic Graphical Models?

The main components of PGMs include nodes, which represent random variables, and edges that denote relationships or dependencies between these variables. These models can be classified as directed or undirected graphs, depending on whether they represent causal relationships or merely associations. Furthermore, each node is associated with a probability function that encapsulates the uncertainty related to that variable. Understanding these components allows users to design effective models tailored to specific applications, such as risk assessment in finance or spam detection in email filtering.

What are some applications of Probabilistic Graphical Models?

Probabilistic Graphical Models have a vast array of applications across various fields. In machine learning, they are used for classification tasks, such as spam detection or image recognition. In bioinformatics, PGMs assist in gene mapping and understanding biological pathways. They also find uses in social network analysis, where they model interactions among users. By leveraging the capabilities of PGMs, organizations can enhance decision-making processes through better insights drawn from complex datasets, such as predicting user behavior on e-commerce platforms.

What is the importance of learning in PGMs?

Learning in Probabilistic Graphical Models is crucial for accurately parameterizing the models based on data. This involves estimating the underlying probability distributions and determining the structure of the graph itself. Popular learning algorithms, such as the Expectation-Maximization algorithm, are frequently employed to optimize these parameters iteratively. Effective learning enables PGMs to adapt to changing data patterns and improve predictions in practical scenarios, like adapting a recommendation system on an e-commerce site based on recent user behaviors.

Can you explain the inference process in PGMs?

Inference in Probabilistic Graphical Models refers to the process of deducing unknown variables from known information within the model. This can take the form of exact inference, using algorithms like variable elimination, or approximate inference, which employs techniques like Markov Chain Monte Carlo (MCMC). This flexibility allows PGMs to be used in real-world applications where some factors are uncertain but can be estimated based on other known variables. For instance, in a weather forecasting model, the inference process may utilize observed data to predict future weather conditions.

What kind of mathematical background is needed for understanding PGMs?

To effectively understand and work with Probabilistic Graphical Models, a solid foundation in probability theory, linear algebra, and statistics is essential. Familiarity with concepts such as random variables, probability distributions, and conditional independence is critical. Additionally, knowledge of graph theory can significantly enhance comprehension regarding the structures of PGMs. Individuals with a background in these areas are better equipped to grasp advanced topics in machine learning and artificial intelligence, where PGMs are frequently applied to solve complex problems.

Are there any software tools for working with PGMs?

Yes, there are several software tools designed specifically for working with Probabilistic Graphical Models. Popular libraries include pgmpy for Python, which provides a comprehensive set of functionalities for building and manipulating PGMs, and TensorFlow Probability, which integrates statistical reasoning into machine learning workflows. These tools allow practitioners to construct models, perform inference, and conduct learning tasks effectively. Using these resources can streamline the process of implementing PGMs in real-world projects, such as predictive modeling in various industries.

What is the significance of the Adaptive Computation and Machine Learning series?

The Adaptive Computation and Machine Learning series is significant in that it compiles cutting-edge research and methodologies in machine learning and artificial intelligence. It provides readers with deep insights into various adaptive algorithms, including those used in Probabilistic Graphical Models. This series is ideal for students, researchers, and practitioners who seek to stay updated on the latest advancements in the field. Engaging with this series can ignite innovative thinking and inspire the application of PGMs in diverse domains such as dynamic systems and intelligent data analysis.

Hvordan shopper man Probabilistic Graphical Models: Principles and online fra Ubuy?

Det er nemt at shoppe Probabilistic Graphical Models: Principles and online fra Ubuy.. Du skal bare søge efter produktet, vælge din forsendelsesmetode, mens du tjekker ud og få det leveret til dit sted.

Hvor lang tid tager det at få produktet efter at have afgivet ordren?

Leveringstiden for dit bestilte produkt varierer alt efter, hvad du har bestilt, og den forsendelsesmetode, du har valgt.. Den estimerede leveringstid er nævnt under købsprocessen, så vær ubekymret, mens du handler.

Se original

Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series)

The framework of probabilistic graphical models provides a general approach for reasoning under uncertainty.

Vare #: 4153811

Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series)

Q: Where can I buy Probabilistic Graphical Models: Principles and Techniques in Faroe Islands?

You can purchase 'Probabilistic Graphical Models: Principles and Techniques' through Ubuy in Faroe Islands. Ubuy offers a wide selection of books, including academic and technical titles, making it a convenient choice for finding specialized literature. Their platform allows you to compare different versions and formats, ensuring that you find the right resource for your needs. By using Ubuy, you benefit from a user-friendly experience tailored for those seeking quality educational materials.

90% af de adspurgte ville anbefale produktet til en ven

Vare #: 4153811

DKK 729

Prisdetaljer

Eksklusive forsendelses- og toldgebyrer ( Forsendelses- og toldgebyrer beregnes ved checkout )

^{*Alle varer importeres fra US}

Tilgængelighed : På lager

Importeret fra USA butik

QTY:

Kun 1 vare tilbage på lager.

Bestil nu og få den omkring Torsdag, Oktober 22

Sikker transaktion

Vores bedste logistikpartnere

The framework of probabilistic graphical models provides a general approach for reasoning under uncertainty.

Hardcover

Paperback

U-Pleje garanti:

Ingen

Vælg en plan

Hurtig
levering

Gratis
returnering^*

Sikker emballering

100 % originale produkter'

PCI DSS Compliance

ISO 27001 Certified

Note: Step Down Voltage Transformer required for using electronics products of US store (110-120). Recommended power converters Køb nu.

Hvad der skiller sig ud

Comprehensive Framework

Provides an in-depth exploration of probabilistic graphical models, offering foundational principles and advanced techniques to enhance understanding and application in various domains.

Practical Applications

Illustrates real-world applications across machine learning, statistics, and artificial intelligence, making complex concepts accessible and relevant to practitioners and researchers.

Current Research Insights

Integrates the latest research developments, allowing readers to stay abreast of cutting-edge methodologies and theoretical advancements within the field.

Produktdetaljer

A general framework for constructing and using probabilistic models of complex systems that would enable a computer to use available information for making decisions.Most tasks require a person or an automated system to reason—to reach conclusions based on available information. The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality. Probabilistic Graphical Models discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data. For each class of models, the text describes the three fundamental cornerstones: representation, inference, and learning, presenting both basic concepts and advanced techniques. Finally, the book considers the use of the proposed framework for causal reasoning and decision making under uncertainty. The main text in each chapter provides the detailed technical development of the key ideas. Most chapters also include boxes with additional material: skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology; and concept boxes, which present significant concepts drawn from the material in the chapter. Instructors (and readers) can group chapters in various combinations, from core topics to more technically advanced material, to suit their particular needs.

Publisher	The MIT Press
Publication date	July 31, 2009
Edition	1st
Language	English
Print length	1231 pages
ISBN-10	0262013193
ISBN-13	978-0262013192
Item Weight	4.63 pounds (2.1 kg)
Reading age	18 years and up
Dimensions	9.22 x 8.18 x 2.05 inches (23.4 x 20.8 x 5.2 cm)
Grade level	12 and up

Hvem bør købe?

Students of AI
Ideal for advanced students studying artificial intelligence and machine learning concepts focused on probabilistic models.
Data Scientists
Data professionals seeking in-depth knowledge of graphical models for data analysis and decision-making processes.
Researchers
Researchers in statistics and machine learning will benefit from comprehensive theoretical and practical insights into graphical models.

Beginners in ML
Not suitable for those new to machine learning, as the content is complex and requires foundational knowledge.

Produktbeskrivelse

Har du spørgsmål? Chat med os

Kundespørgsmål og svar

spørgsmål: Hvordan shopper man Probabilistic Graphical Models: Principles and online fra Ubuy?

svar: Det er nemt at shoppe Probabilistic Graphical Models: Principles and online fra Ubuy.. Du skal bare søge efter produktet, vælge din forsendelsesmetode, mens du tjekker ud og få det leveret til dit sted.
spørgsmål: Er Probabilistic Graphical Models: Principles and tilgængelig til at handle online i Faroe Islands?

svar: Ja, hos Ubuy Faroe Islands er dette produkt tilgængeligt, så du kan handle til en rimelig pris.. Probabilistic Graphical Models: Principles and er ikke tilgængelig lokalt, men du kan stole på os med vores ekspresforsendelsestjenester.
spørgsmål: Hvor lang tid tager det at få produktet efter at have afgivet ordren?

svar: Leveringstiden for dit bestilte produkt varierer alt efter, hvad du har bestilt, og den forsendelsesmetode, du har valgt.. Den estimerede leveringstid er nævnt under købsprocessen, så vær ubekymret, mens du handler.

Intelligence & Semantics Editorial Review

Probabilistic Graphical Models: Principles and Techniques is a comprehensive textbook that may require some effort to read due to its verbosity and small font size. However, it provides in-depth content on the theory of PGMs and is a valuable reference for those interested in the subject. The book focuses on the mathematical and theoretical aspects of PGMs, rather than practical applications or coding. While some readers may find the writing style and grammar unconventional, the book's diligence in explaining the subject makes it a classic text for machine learning.

Kundeanmeldelser & vurderinger

4.5

127 kundebedømmelser

5 stjerne

73%
4 stjerne

14%
3 stjerne

6%
2 stjerne

5%
1 stjerne

2%

Anmeld dette produkt

Del dine tanker med andre kunder

Fordele

Comprehensive content
Valuable reference for PGMs
In-depth coverage of theory
Diligent explanation of the subject

Ulemper

Verbose and cumbersome

Produktets prishistorik

Vigtig information

Begrænsninger: For produkter, der forsendes internationalt, bemærk venligst, at eventuel producentgaranti muligvis ikke er gyldig; producentens serviceordninger er muligvis ikke tilgængelige; produktmanualer, instruktioner og sikkerhedsadvarsler er muligvis ikke på destinationslandets sprog; produkterne (og det medfølgende materiale) er muligvis ikke designet i overensstemmelse med destinationslandets standarder, specifikationer og mærkningskrav; og produkterne opfylder muligvis ikke destinationslandets krav til spænding og andre elektriske standarder (hvilket kan kræve brug af en adapter eller transformer, hvis relevant). Modtageren er ansvarlig for at sikre, at produktet lovligt kan importeres til destinationslandet. Ved bestilling hos Ubuy eller dets datterselskaber er modtageren importøren og skal overholde alle love og regler i destinationslandet.
Det er ikke alle de produkter som vises på Ubuy som er til salg, idet Ubuy er en global søgemaskine. Produkterne er underlagt eksport-/handelsregler.

DKK 729

Bestil nu og få den omkring Torsdag, Oktober 22

QTY:

Kun 1 vare tilbage på lager.

Sikker transaktion

PCI DSS-kompatibel og ISO 27001:2022-certificeret, med krypteret betaling og fuld køberbeskyttelse på hver ordre

Vores bedste logistikpartnere

Hurtigste grænseoverskridende levering

Egenskaber og fordele

Offers a comprehensive framework for probabilistic modeling.
Enables automated systems to make informed decisions.
Focuses on making uncertainty explicit in modeling.
Covers Bayesian networks, Markov networks, and more.
Includes practical techniques and case studies for real-world applications.
Flexible structure allows customization for diverse learning needs.

Ubuy-garanti

Oplev bekymringsfri shopping med 100 % originale produkter, PCI DSS-kompatibel betalingssikkerhed, ISO 27001-certificeret databeskyttelse, den hurtigste grænseoverskridende levering, gratis returnering* og sikker emballage på hver ordre.

Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series)

DKK 729

Hurtig levering

Gratis returnering*

Sikker emballering

100 % originale produkter'

PCI DSS Compliance

ISO 27001 Certified

Hvad der skiller sig ud

Produktdetaljer

Hvem bør købe?

Produktbeskrivelse

Kundespørgsmål og svar

Intelligence & Semantics Editorial Review

Kundeanmeldelser & vurderinger

Anmeld dette produkt

Fordele

Ulemper

Produktets prishistorik

Vigtig information

DKK 729

Egenskaber og fordele

Ubuy-garanti

Hurtig
levering

Gratis
returnering^*