Bayesian trabalhos

Bayesian linear regression plot; svd for principal component plot. please check the attached file and let me know if you can do them perfectly

...a range of statistical and probability calculations. Key Requirements: - The code should be thoroughly tested and debugged to ensure it functions correctly - The code should be capable of performing complex statistical and probability calculations, such as: - Descriptive statistics - Hypothesis testing - Regression analysis - Probability distributions - Conditional probability - Bayesian inference Ideal Skills: - Expertise in Python programming - Strong background in statistics - Experience with statistical libraries in Python (e.g., NumPy, SciPy, Pandas) - Attention to detail and ability to debug code effectively - Strong problem-solving skills and the ability to troubleshoot Please provide examples of previous work, particularly any projects that involve statist...

I'm seeking a proficient statistic...influencing housing prices in the Indian context 2. To measure the impact of each factor on housing prices 3. To identify variations in housing prices corresponding to property attributes Data will be provided you are expected to clean and format the data making it ready for analysis, By Hedonic Pricing Model, creating Multiple regression models, and then the Best model to be identified by BMA (Bayesian Model Averaging), and finally Computing LMG to determine the degree of importance of each variable A sample paper of the expected process will be provided. Looking forward to connecting.. By using your statistical expertise, you'll help me reveal the hidden insights in my data and contribute to my scientific research. I look forwar...

I'm looking for an expert in Bayesian networks to analyze mixed data types, specifically focusing on identifying patterns within numerical and categorical data. This project aims to uncover underlying structures and patterns that could inform future decision-making processes. **Key Skills and Experience Required:** - Proficient in Bayesian network models (especially in dbnR package or dbnlearn) - Experience with mixed data types, particularly numerical and categorical data - Strong background in data analysis and pattern recognition - Proficiency in programming languages R, especially libraries relevant to Bayesian statistics and data visualization - Ability to interpret complex data sets and provide clear, actionable insights **Project Goals:** - To analyze a ...

The aim of this project is to apply Bayesian modelling to the movement of a ping-pong ball. Project Outcomes: 1.PDFreport. 2.R Markdown(.Rmd) notebook. This project requires advance knowledge in Bayesian modelling and R programming with STAN and JAGS.

...data. Ideal skills for this project would include: - A strong background in MATLAB programming - Experience in data visualization, more specifically numeric and time-series data - Proficiency in dealing with databases and parsing CSVs - Understanding of mathematical modeling concepts - Requires a data input and a function to run an existing code completed in Matlab -The Matlab program implements Bayesian inference and makes predictions based on observed data Your role would be to design this application from the ground up, building robust features and ensuring optimal performance. I’d expect you to have a proven track record in similar projects and a strong understanding of data visualization techniques and mathematical modeling processes. Your ability to deliver a user-f...

Do the two tasks: 1) BDA Analytics (same as previous time but in Python, Jupiter notebook) 2) Bayesian network part.

As we discussed, 750 pounds and 1 day

Project Description: Titanic survival prediction using Bayesian Networks Skills and Experience: - Proficiency in Python programming language - Familiarity with machine learning libraries such as Scikit-learn, TensorFlow, and PyTorch - Strong understanding of Bayesian Networks and their application in predictive modeling Project Requirements: - Develop a predictive model using Bayesian Networks to determine the likelihood of survival for passengers on the Titanic - Utilize the preferred programming language, Python, for implementation - jupyter notebook must be used - Aim for a desired accuracy level for the prediction model (to be determined by the client)

I'm looking for a data analyst to perform risk analysis on my Excel and Power BI dataset using a Bayesian approach. I prefer using Excel and Power BI as tools for this project, so strong knowledge in both software is required. Although I've not provided extensive details about the dataset, it's crucial for you to be adaptable and well-versed in dealing with data size and structure, variable types, and potential data cleaning.

...The key features include AI-powered student proficiency tracking using Natural Language Processing (NLP) for deeper understanding, Bayesian Networks or Markov Decision Processes for assessing strengths/weaknesses, and Reinforcement Learning to dynamically adapt question difficulty and timing. The module also includes adaptive question generation using AI to select questions matching the student's updated level and AI-driven performance assessment to automatically identify incorrect answer patterns and common mistakes. Key Features: • AI-powered Student Proficiency Tracking: o NLP analyses student explanations for deeper understanding of weaknesses. o Bayesian Networks or Markov Decision Processes assess strengths/weaknesses based on response patterns and diffic...

I need to draw a Bayesian network (Bayes net) for the model described:

I am looking for a Statistician who has at least Master degree in statistics with advanced level in R, to write codes for equation related to Bayesian Inference, Gibbs Sampler. The Max payment is 60 $.

I am looking for a skilled Python developer who can help me with a proj...currently implementing a Prophet model for forecasting but I would like to see if a probabilistic method would be better. I will share that code with you which you will build upon or add to in the same notebook to add the probabilistic forecast code. I would like the output to include various forecasts with their associated probabilities and mape comparisons to the Prophet model. Although I prefer a Bayesian probabilistic model, I am open to any method. The timeframe for the forecast is short-term. It would be best if you experience working with probabilistic forecasting models. If you have the necessary skills and experience, please submit your proposal. I look forward to discussing the project further...

I am looking for a programmer with expertise in C++ to assist me with a specific task. The project needs to be done from next Jan to Feb. Task details: The main task is to build a Bayesian network based on exiting API code (with languages of either JAVA or C++) with three to five key algorithms adjust (I have the algorithms), data input, model operation and results test. Since the funding is limited, here are two options for me: 1. All programming are entrusted to the programmer. 2. A detailed programming guide for me. Skills and Experience: - Proficiency in C++ - Strong problem-solving skills - Experience in developing and debugging C++ code - Knowledge of data structures and algorithms - Ability to work within tight deadlines

I am looking for a freelancer who can help me with a project on Bayesian changepoint detection using the R programming language. I have a specific dataset that I would like to use for this project. As for the method of implementing the Bayesian changepoint detection, with metropolis-hhastings sampler. Ideal Skills and Experience: - Proficiency in R programming language - Experience with Bayesian changepoint detection - Knowledge of different methods for implementing Bayesian changepoint detection - Ability to work with specific datasets provided by the client

Skills needed for the project: Data Analysis: Analyzing and interpreting data. Regression Modeling: Building and interpreting regression models. Model Validation: Comparing models using validation techniques. Prediction and Confidence Intervals: Making predictions and calculating confidence intervals. Probability Concepts: Understanding confidence intervals and Bayesian vs. frequentist approaches. Bayesian Analysis: Applying Bayes' rule to decision-making scenarios. Simulation Techniques: Using simulations (like Monte Carlo) for analytical validation.

dataset/109/wine. The dataset is continuous and therefore discretization would be required to build the network. Build classification model based on class variable in the data for performance evalua- tion (accuracy) use bnlearn

ML Notebook for atleast ten CNN Hyperparameter Tuning using Bayesian optimization, Random Search and Grid search. I will provide dataset to use. Skills and Experience Required: - Proficiency in Python programming language - Experience with convolutional neural network (CNN) architectures, specifically pre-trained models - Knowledge of image classification techniques Project Description: I am looking for a skilled ML expert to create a ML notebook for hyperparameter tuning of a convolutional neural network (CNN). The notebook should be written in Python and should focus on using pre-trained CNN architectures for image classification. Requirements: 1. Programming Language: Python 2. CNN Architecture: Pre-trained 3. Application: Image Classification The ideal candidate for this pr...

I am looking for a freelancer who can assist me with Bayesian Prior Elicitation. The purpose of this project is to estimate parameters with limited data. Specifically, I have specific statistical data and models that need to be used for this task. The deadline for this project is within 1 week. Ideal Skills and Experience: - Strong understanding of Bayesian statistics and prior elicitation - Proficiency in statistical modeling and data analysis - Experience in working with limited data and parameter estimation - Ability to work within tight deadlines and deliver high-quality results

...fundamentals - Aware of optimization techniques and differences (gradient decent variants, hyperparameter tuning, regularization) - Some experience with different problems and domains, ideally specialized in at least one (supervised, unsupervised, generative, control/RL, anomaly detection, timeseries, etc.) - Familiarity with a couple of traditional ML models (k-means, KNN, decision trees, SVM, Bayesian/graphical models, Gaussian process, etc.) - Basic understanding of deep learning fundamentals (CNN, RNN, attention/memory, Autoregressive) and ideally some applications of extensions (Transformer, LSTM, ResNet, etc.) - Experience coding with popular frameworks - Solid with Python (basic data structures, coding patterns) along with common data, math and visualization libraries - Ab...

I am seeking a skilled professional who can help me improve the accuracy of my Bayesian neural network. The current accuracy of the network is more than 70%, and I am looking to make further enhancements. The model is based off of the "Incorporating Interpretable Output Constraints in Bayesian Neural Networks" paper, and I am planning to use the neural network on proprietary dataset once better accuracy is reached on the publicly available MIMIC III dataset. Please Note: any incremental improvement in accuracy will be paid the full amount of the freelance work, but a mathematical reasoning for what changes were applied needs to be there. Dataset: - I am currently using a publicly available dataset for both training and testing - the MIMIC III dataset. Desired A...

...deliver this project: The chatbot will be called Billie. Billie allows a user to have a conversation with a bayesian belief network. An rough example of the conversation that Billie should be able to have is attached. The example is essentially the specification that will be provided, although I am very happy to answer questions for the appointed developer. The bayesian belief network is defined within a json file (example attached). The json file includes details of the nodes, node states, CPTs, and node parents - which are required to define the bayesian network. The json file also optionally includes descriptions of nodes, node states, and of the model itself. These are all used by the chatbot. The developer is expected to

I am looking for experienced aptitude in Python coder to help me with a PGM project which is creating a movie recommendation system using Bayesian network. I have specific requirements that must be met, and I require the project completed in a time span of 15 days. Therefore, freelancers with the adequate technical expertise, experience and availability within the stated timeline are preferred. As the client, I trust that the freelancer I end up choosing for the project is reliable and proficient in their craft. In return for taking on this project, you will receive a fair remuneration commensurate with your skills and experience. Thank you for considering this opportunity.

Project Description: We are building a comprehensive risk analysis software that leverages the power of Bayesian Networks and the Neo4j graph database. We are currently seeking an experienced developer to design and implement Data Access Objects (DAOs) for our application. Key Responsibilities: 1. Collaborate with our team to understand the application's data model and requirements. 2. Design and implement DAOs for various entities such as Risks, Causes, Consequences, Risk Controls, etc. 3. Ensure efficient and optimized access to the Neo4j database, keeping performance considerations in mind. 4. Implement CRUD operations for each DAO, ensuring data integrity and consistency. 5. Write unit tests to validate the functionality and reliability of the DAOs. 6. Document code and ...

...indicators for every stock in the S&P 500 from its IPO date, testing all possible indicator 'settings', as I prefer to call them. This process uncovers microscopic signals that suggest beneficial buying at market close and selling at the next day's close. Any signal with a p-value below 0.01 is added to my portfolio. Following this, the system removes correlated signals to prevent duplication. A Bayesian ranking of signals is calculated, and correlated signals with a lower rank are eliminated. The result is a daily optimized portfolio of buy/sell signals. This system, primarily built with numpy python, doesn’t rely on 'black-box' libraries. I am seeking an experienced python developer to help adapt my existing algorithm to scale when work...

I have a project to code a Bayesian optimiser which should be used to maximise an unknown blackbox function, taking known inputs and known outputs to do so. It needs to be able to either recommend the next best inputs to try or allow the user to enter them. Additionally, it should also provide a 3D plot and allow different parameters to be changed. For this project, I would prefer the freelancer to use Python as their programming language and the Scikit-learn library/framework for the optimisation process. The 3D plot should be an interactive one.

...long period, which I utilize to compute the EV. A unique challenge I've encountered is the dynamic nature of the optimal stop loss percentage, which appears to vary depending on the current stock price. A few observations include: 1. At a stock price of 1 dollar, the optimal stop loss might be 5%. 2. At 5 dollars, it might be 10%. 3. And at 5.5 dollars, it could be 7%. I've been exploring Bayesian Optimization as a potential solution, yet I'm uncertain about its efficacy given the inherent dynamic nature of the optimal parameter. Another consideration has been using a neural network to predict the optimal stop loss percentage in relation to the stock price. Given the constraints of my specific model and the aim to optimize dynamically based on stock price, a...

...work. We provide services in the domain of Machine Learning, AI, Deep Learning, Computer Vision, Natural Language Processing, Algo Trading. Expertise: Training Machine learning, Deep Learning, Reinforcement Learning Models Frameworks are compatible with Pytorch, Tensorflow, Keras. Training ML models like Bayesian Networks, XGBoost, Random Forest, Decision Tree, KNN Classifier, GBM, GLM, SVM. Training DL models BERT and all its variants, DNN, CNN, LSTM, GRU, Variational Autoencoders using Bayesian Neural Networks. eXplainable AI (XAI) using LIME, SHAP, GRAD-CAM, DeepLIFT, DeepRED, CausalNex. Here are some of our previous works: Hair transplant startups require some image generation for their customers. Masters Thesis in Art Generation using GANs Head Gesture recognition usi...

Bayesian Inference for Academic Research using Excel Skills required: - Strong understanding of Bayesian Inference - Proficiency in Excel - Experience in academic research Project Overview: - I am looking for a freelancer who has a proficient level of expertise in Bayesian Inference to assist me with an academic research project. - The specific purpose of this project is to apply Bayesian Inference techniques to analyze a dataset that I have. - The freelancer should have a strong understanding of Bayesian Inference and be proficient in using Excel for data analysis. - The project requires the freelancer to apply Bayesian Inference methods to the dataset and provide insights and conclusions based on the analysis. - The dataset will be provided by m...

Bayesian Inference for Academic Research using Excel Skills required: - Strong understanding of Bayesian Inference - Proficiency in Excel - Experience in academic research Project Overview: - I am looking for a freelancer who has a proficient level of expertise in Bayesian Inference to assist me with an academic research project. - The specific purpose of this project is to apply Bayesian Inference techniques to analyze a dataset that I have. - The freelancer should have a strong understanding of Bayesian Inference and be proficient in using Excel for data analysis. - The project requires the freelancer to apply Bayesian Inference methods to the dataset and provide insights and conclusions based on the analysis. - The dataset will be provided by m...

Bayesian Inference for Academic Research using Excel Skills required: - Strong understanding of Bayesian Inference - Proficiency in Excel - Experience in academic research Project Overview: - I am looking for a freelancer who has a proficient level of expertise in Bayesian Inference to assist me with an academic research project. - The specific purpose of this project is to apply Bayesian Inference techniques to analyze a dataset that I have. - The freelancer should have a strong understanding of Bayesian Inference and be proficient in using Excel for data analysis. - The project requires the freelancer to apply Bayesian Inference methods to the dataset and provide insights and conclusions based on the analysis. - The dataset will be provided by m...

Bayesian Inference for Academic Research using Excel Skills required: - Strong understanding of Bayesian Inference - Proficiency in Excel - Experience in academic research Project Overview: - I am looking for a freelancer who has a proficient level of expertise in Bayesian Inference to assist me with an academic research project. - The specific purpose of this project is to apply Bayesian Inference techniques to analyze a dataset that I have. - The freelancer should have a strong understanding of Bayesian Inference and be proficient in using Excel for data analysis. - The project requires the freelancer to apply Bayesian Inference methods to the dataset and provide insights and conclusions based on the analysis. - The dataset will be provided by...

...models. These models will then be trained with 0 to 300 cycles and predict 301 to 600 cycles of data. The Graphical representation is attached. The goal of the project is to train all 4 models and then this models will be used to predict the data for other supercapacitor's with minimum possible error values (rmse). All codes should be in MATLAB and should incorporate hyperparameter tuning using Bayesian optimization. For better understanding scan the QR code attached and go through the blogs of this project. 1)Convolutional Neural Networks (CNNs) 2)LSTM 3)GRU 4)Transformer Specific requirements for the project include: - Experience with RNN and its application in studying degradation in supercapacitors - Knowledge of supercapacitor models specified by the client - Ability ...

Descriptive statistics of data, summary of posterior distribution parameter estimation): mean or median and 95% credible interval of the distribution. All the values of the Bayes factors (hypothesis testing), Interpret the results in terms of the evidence in favour or against the hypothesis that training improves performance in Bayesian reasoning over the two control conditions

Project Description: I seek a skilled data scientist to help me develop an air quality prediction model using LSTM with Bayesian optimization and random search hyperparameter tuning. Data Source: - The model will be trained using historical data. Model Selection: - We will use Long-Short Term Memory (LSTM) with Bayesian optimization and random search hyperparameter tuning for the prediction. Expected Output: - The expected output of the model is currently not specified. Ideal Skills and Experience: - Strong knowledge and experience in data science and machine learning - Expertise in LSTM models and optimization techniques - Proficiency in Bayesian optimization and random search hyperparameter tuning - Familiarity with air quality prediction and relevant data sou...

I am in need of an app development project for a desktop application on the Windows platform. The app will be based on a specific algorithm model that I have in mind and I would like it to be completed as soon as possible. Skills and experience required for this project include: - Strong knowledge in ML and coding of models based on Random Forest, RNN and Bayesian algorithms - Expertise in implementing specific algorithm models - Ability to communicate flawlessly in English work efficiently and deliver high-quality results within a short timeframe.

I am looking for a talented individual to help with a research and analysis project to write a Compartment Model Analysis (SEIR Model - Susceptible, Exposed, Infected, Removed) to estimate the Tuberculosis death rate from year 2000-2030, using ODE and Bayesian Inference in R-STAN. I have existing data from 2000-2022 and want the model to estimate the death for 2023-2030. I need the posterior modeled as a Negative Binomial with Truncated Normal as priors. The task requires a motivated and detail-oriented person who can complete the project within 1-2 weeks. If you are up to the challenge, please get in touch!

Using machine learning, cloud computing and Bayesian Networks for early prediction of a medical condition

I am looking for a freelancer who can create a Bayesian network in GeNIE format for predictive modeling. The purpose of this project is to analyze and predict various outcomes based on the provided data. Requirements: - Proficiency in Bayesian network modeling - Familiarity with GeNIE format - Ability to perform attribute influence analysis using Orange The client has not yet identified any specific variables or attributes for the network, so the freelancer will need to work closely with the client to determine the appropriate variables to include. Communication will primarily be done through email, so the freelancer should be responsive and able to provide updates and answer any questions in a timely manner.

We are currently embarking on a project that requires in-depth data analysis of flow cytometry data using Bayesian inference, and are in need of a talented and experienced freelance analyst to join us in this venture. This project will specifically involve using R for the data analysis, so proficiency in this area is crucial. The successful candidate will possess the following qualifications and skills: Expertise in flow cytometry: Detailed understanding of the principles and applications of flow cytometry, including data acquisition and gating strategies. Bayesian Inference Proficiency: Extensive experience in applying Bayesian methods to real-world problems. Understanding of Bayesian statistics and ability to interpret the results accurately. Strong Backg...

...for someone who is good with who can help me write a simple Bayesian analysis function in Tensor. I am building this tool almost exclusively to the specifications available on this page: The function is to calculate a patient's most likely clearance and volume of distribution and accepts the following priors from external models: Volume of Distribution (average size of patients in the model). Clearance (average rate at which patients in the model clear the drug). It also accepts either one or more drug levels from the patient to help match them to the model. The model's standard deviations are also available (see the linked page). I need the Bayesian model to return the most likely clearance and volume of distribution for that

I am looking for a programmer in R with expertise in Bayesian analysis and JAGS. The project involves model development, and I have already collected the data for it. The ideal candidate must be able to work within the deadline of one week. Skills and experience required for the job include: - Expertise in R programming and Bayesian analysis - Experience with JAGS - Ability to develop statistical models - Strong data analysis skills - Ability to work within tight deadlines.

I am looking for a freelancer who is skilled in R programming for a statistical modeling project. The goal of the project is to analyze and model data using R programming. The freelancer will need to find a...modeling project. The goal of the project is to analyze and model data using R programming. The freelancer will need to find a suitable dataset to work with. The level of complexity required for the statistical modeling is intermediate. The ideal candidate should have experience in statistical modeling using R programming and be able to find and manipulate datasets. You will be required to do a Bayesian analysis. Data that has to be taken should be of Newzealand. Not less that 10 variables. 3 years of data. Data should be on 3 things, so 3 data needed. On weather, pollution a...

...engagement and progress. The platform includes user authentication and authorization, course management, unit and lesson management, text selection, sentiment analysis, Bayesian networks, and adaptive learning capabilities. The platform will use Vimeo API for video playback and Stripe for payment processing. Target Audience: The target audience for the LMS is for students and instructors that adhere to a popular international curriculum. The platform will provide support for students with learning disabilities such as ADHD, dyslexia, and graphophobia. Key Features: Adaptive learning model based on sentiment analysis and Bayesian networks to personalize the learning experience. Gamification features and pages including achievements, milestones, power-ups, ...

I already have code in pytorch but able to find out the error.

I am looking for someone to help me with my Bayesian network (BN) and data analytics project. I have existing data that needs to be analyzed. This data doesn't have to fall into a specific type - it can be textual, financial, or structured. The end product of the analysis should be in some kind of digital format, most likely Excel, PowerPoint, or PDF. However, that is up to the discretion of the freelancer. I'm looking for someone who is experienced in data analytics and can present their findings in an organized and comprehensive way.

I'm looking for an experienced AI programmer to help me with a project involving exact inference, rejection sampling, Gibbs sampling inference and approximate inference in a Bayesian network, using C++. The project will involve using the acceptance-rejection sampling method to perform approximate inference, the Gibbs sampling algorithm and exact inference. The final goal of this project is to develop a reliable and efficient program that performs these tasks accurately and quickly. The 3 methods to be implemented are in the file ReseauBayesien.h. I have an allotted budget, so please make sure to provide an honest and reasonable quote when submitting a proposal. I'm looking forward to receiving your offers and finding the perfect partner for this project!

...to the observed data in terms of summary statistics and overall structure. - Ensure that the model demonstrates satisfactory convergence by producing realistic simulations. - Include functionality to visualize and save posterior predictive check results. 7/. Widely Applicable Information Criterion (WAIC) and Deviance Information Criterion (DIC): - Calculate the WAIC and DIC for the fitted Bayesian model. - Ensure that the WAIC and DIC values are reasonably low, indicating a good balance between model fit and complexity. - Track the WAIC and DIC values during model selection, comparing different model configurations to choose the best one. - Provide a summary report of WAIC and DIC values for different models and include functionality to export these values. Deliverables: ...

I need help in refining a project I have done: