HYBRID EVENT
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Hamad Town, Bahrain
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ICBMIS · Registering as Listener
International Conference on Bayesian Modeling and Inference in Statistics
28–29 Jun 2027
Hamad Town, Bahrain
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$
135
virtual · $195 in person
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$195
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$135
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1. Policy on Cancellations & Refunds :
A full refund is possible only if the cancellation request is submitted at least 70 days before the conference, with formal paperwork completed at least 60 days prior.
If cancellation occurs between 60 and 30 days before the event, partial refunds may be granted based on administrative costs incurred.
Cancellations made within 30 days of the event are non-refundable, though participants will receive a credit valid for one year.
Registrations made less than 30 days before the event are not eligible for refunds, but may be transferred to another ISIT conference.
Since this conference is hybrid format, the organizer reserves the right to conduct the event either in-person or virtually. Please note that no refunds will be issued due to changes in the event format.
Virtual registration fees are non-refundable. A credit will instead be issued for use at a future conference event.
2. Participation & Registration Requirements :
To attend an ISIT event, participants must complete the registration process within the stipulated time.
The event schedule, venue, and format are subject to changes at the discretion of the organizers, with prior notice sent via email.
ISIT is not liable for any financial losses resulting from changes in event details.
Fees paid for registration are strictly non-refundable.
If the primary author cannot participate, a co-author may attend instead, but refunds will not be granted for non-attendance.
3. Submission & Publication Norms :
Researchers submitting papers to ISIT must ensure their institution or supervisor is aware of their submission.
Each submission is subject to a rigorous peer review before being accepted for presentation.
Only papers linked to a completed registration will be included in conference proceedings.
The submitting author is considered the primary author; ISIT does not verify individual authorship claims.
If any concerns regarding authorship arise and are validated, the paper will be withdrawn without reconsideration.
Once a paper is removed, it cannot be reintroduced into any ISIT publication.
4. Travel & Accommodation Responsibilities :
Attendees are responsible for making their own travel and lodging arrangements.
ISIT does not provide logistical assistance for travel or accommodation.
The organization bears no responsibility for expenses incurred due to conference modifications or rescheduling.
5. Visa & Invitation Letter Policy :
Attendees are responsible for making their own travel and lodging arrangements.
ISIT does not provide logistical assistance for travel or accommodation.
The organization bears no responsibility for expenses incurred due to conference modifications or rescheduling.
ISIT does not engage directly with consulates or embassies on behalf of attendees.
Participants must handle their own visa applications and processes.
Important Information Regarding Invitation Letters:
The invitation letter is provided solely for conference attendance and does not serve as a document for immigration, employment, or residency purposes.
The letter assists in visa applications but does not guarantee visa approval.
ISIT holds no responsibility for visa denials or processing delays, and all related costs are borne by the applicant.
Any alterations or unauthorized use of the invitation letter will result in its invalidation and possible cancellation of conference participation.
Legal action may be pursued if the document is misused.
By accepting the invitation letter, attendees agree to comply with international travel regulations and ethical participation standards.
6. Registration Transfers :
Registrations may be transferred to another individual from the same institution if the original participant cannot attend.
Transfer requests must be made via email to
[email protected]
with necessary details and supporting documents.
Transfers must be requested at least 14 days before the event; otherwise, they will not be accommodated.
Transferred registrations are not eligible for refunds.
7. General Considerations :
Any modifications or cancellations must be communicated in writing to
[email protected]
.
Registration confirms acknowledgment and acceptance of these policies.
ISIT does not initiate automatic transactions; all payments are voluntarily completed by registrants.
Once registered, participants must submit a conference registration form within three days for confirmation.
Travel plans should only be finalized after receiving the official conference itinerary, which will be shared 15 days before the event.
8. Conference Programme and Participation Policy :
Presentations from different but relevant academic areas may be combined by the Organiser within an interdisciplinary or multidisciplinary programme to encourage cross-disciplinary understanding, interaction, and future research partnerships.
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What you'll hear about
Session tracks covered across the event.
View all 11 tracks →
01
Advancements in Bayesian Modeling Techniques
This track focuses on the latest developments in Bayesian modeling methodologies, emphasizing novel approaches and theoretical advancements. Researchers are encouraged to present innovative techniques that enhance the flexibility and applicability of Bayesian models.
02
Statistical Inference in Complex Data Structures
This session aims to explore statistical inference methods tailored for complex data structures, including hierarchical and multilevel models. Contributions that address the challenges of inference in high-dimensional and structured data are particularly welcome.
03
Machine Learning and Bayesian Approaches
This track investigates the intersection of machine learning and Bayesian inference, highlighting how Bayesian methods can enhance learning algorithms. Topics may include Bayesian neural networks, probabilistic graphical models, and uncertainty quantification in machine learning.
04
Predictive Modeling with Bayesian Frameworks
This session is dedicated to the application of Bayesian frameworks in predictive modeling across various domains. Papers that demonstrate the effectiveness of Bayesian methods in improving prediction accuracy and model interpretability are encouraged.
05
Markov Chains and Monte Carlo Methods
This track delves into the theoretical and practical aspects of Markov chains and Monte Carlo methods in Bayesian statistics. Contributions that explore new algorithms, convergence properties, and applications in complex models are sought.
06
Bayesian Networks and Graphical Models
This session focuses on the development and application of Bayesian networks and other graphical models for statistical inference. Researchers are invited to present work that advances the understanding of dependencies and causal relationships in data.
07
Prior Distributions and Posterior Estimation
This track examines the role of prior distributions in Bayesian analysis and their impact on posterior estimation. Papers that propose new priors, discuss prior sensitivity, or explore empirical Bayes methods are particularly relevant.
08
Computational Statistics and Bayesian Inference
This session highlights computational techniques that facilitate Bayesian inference, including algorithms for high-dimensional data and large-scale models. Contributions that address computational challenges and improve efficiency in Bayesian analysis are encouraged.
09
Quantitative Methods in Bayesian Research
This track focuses on quantitative methods that enhance Bayesian research, including statistical techniques and data analysis strategies. Papers that showcase innovative applications of quantitative methods in various fields are welcome.
10
Applications of Bayesian Inference in Real-World Problems
This session seeks to highlight the practical applications of Bayesian inference across diverse fields such as healthcare, finance, and environmental science. Researchers are invited to share case studies and empirical research that demonstrate the utility of Bayesian methods.
11
Emerging Trends in Bayesian Data Science
This track explores emerging trends and future directions in Bayesian data science, including the integration of artificial intelligence and big data analytics. Contributions that discuss innovative applications and theoretical advancements in this rapidly evolving field are encouraged.