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1) The document discusses using informative priors to improve parameter estimation in capture-recapture models used to estimate the population size of brown bears in the French and Spanish Pyrenees. 2) A Jolly-Seber model was used on capture-recapture data collected over 25 years to estimate population size and test for differences between two population cores. An informative prior was used to improve precision for the small population. 3) Results showed no difference in estimates when using informative versus non-informative priors for the full dataset and individual population cores. But for shorter datasets, the informative prior reduced standard deviation, showing its usefulness for smaller datasets.

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A basic Introduction To Statistics with examples

A basic Introduction To Statistics with examples

Introduction to Biostatistics

Introduction to Biostatistics

LECTURE 3-PREVALENCE& DISEASES OUTBREAK.pdf

LECTURE 3-PREVALENCE& DISEASES OUTBREAK.pdf

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A basic Introduction To Statistics with examples

A basic Introduction To Statistics with examples

Introduction to Biostatistics

An introductory course of biostatistics lectured for the Master of Healthcare.
This chapter is the first chapter of a whole program of 25 chapters divided into 4 sections described in this presentation.

LECTURE 3-PREVALENCE& DISEASES OUTBREAK.pdf

This document provides guidance on designing a study to measure disease prevalence. It outlines five key steps: 1) defining study objectives, 2) designating a sampling strategy, 3) preparing data collection tools, 4) data management, and 5) data analysis and reporting. Important considerations for sampling strategy include determining eligibility criteria, constructing a sampling frame, and selecting an appropriate sampling method such as simple random sampling. Sample size is determined based on desired precision, expected prevalence, and confidence level. Statistical analyses are used to evaluate results by testing hypotheses and determining significance based on p-values.

Ph250b.14 measures of disease part 2 fri sep 5 2014

This document outlines learning objectives and concepts related to measuring disease in epidemiology. It discusses different types of populations, concepts of disease occurrence over time, and key epidemiologic measures including prevalence, incidence, risk, rates, and methods for calculating cumulative incidence. Cumulative incidence can be calculated using simple, actuarial, Kaplan-Meier, or density methods, each with different assumptions about follow-up time and censoring. The relationships between prevalence, incidence, and risk/rates are also reviewed.

San diego

This document describes a center sampling technique for estimating characteristics of populations where complete lists are unavailable, such as illegal migrants. It involves sampling from aggregation centers that individuals are likely to interact with, such as schools or healthcare facilities. Weights are calculated based on the probability of individuals interacting with each center to estimate population proportions from the sample. Simulations and applications to Italian data on illegal migrants are discussed. The center sampling method provides a way to estimate population features when standard sampling is not possible due to incomplete population lists.

Statics for management

Statistics is the mathematical science of collecting, analyzing, and interpreting data. It provides tools for describing data, making inferences and predictions, and testing hypotheses. The document distinguishes between primary and secondary data, and defines various statistical terms like sample, variable, and population. It also provides examples of solving statistical problems like finding the mean, median, and standard deviation of data sets, and calculating probabilities.

Sampling Theory Part 1

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Bio stat

This document provides an introduction and overview of biostatistics. It defines key biostatistics terms like population, sample, parameter, statistic, quantitative vs. qualitative data, levels of measurement, descriptive vs. inferential biostatistics, and common statistical notations. It also discusses sources of health information and how computerized health management information systems are used to collect, analyze and report data.

Sampling

concept of sample and sampling, sampling process and problems, types of samples: probability and non probability sampling, determination and sample size, sampling and non sampling errors

Statistics for management assignment

This document provides information on statistics and probability sampling methods. It defines statistics as the science of collecting, organizing, summarizing, analyzing, and interpreting data. It describes the four main components of statistics as data collection, presentation, analysis, and interpretation. It also lists seven key characteristics of statistics. The document then discusses probability concepts like probability, experiments, outcomes, and definitions. It provides an example to calculate probabilities. Finally, it describes various probability sampling methods like simple random sampling, stratified random sampling, systematic sampling, cluster sampling, and multi-stage sampling as well as non-probability sampling methods like judgment sampling, convenience sampling, and quota sampling.

Seminar-2015

This document outlines a study that aims to evaluate long-term outcomes of periodic cancer screening, including the inference of overdiagnosis. It describes using a probability model and simulation based on data from the HIP study to derive the probability of each long-term outcome: symptom-free life, no early detection, true early detection, and overdiagnosis. The key outcomes are defined and equations are provided to calculate the probability of each outcome based on factors like screening sensitivity, sojourn time in preclinical and clinical states, and a person's lifetime as a random variable. The methodology aims to investigate the chance of overdiagnosis from continued screening and evaluate long-term effects for an entire screened cohort.

SP and R.pptx

This document provides information about statistics and probability. It defines statistics as the collection, analysis, and interpretation of data. There are two main categories of statistics: descriptive statistics, which summarizes and describes data, and inferential statistics, which is used to estimate, predict, and generalize results. The document also discusses population and sample, measures of central tendency (mean, median, mode), measures of dispersion (range, variance, standard deviation), qualitative vs. quantitative data, ways of representing quantitative data (numerically and graphically), and examples of organizing data using a stem-and-leaf plot.

Introduction to Statistics (Part -I)

This presentation covers statistics, its importance, its applications, branches of statistics, basic concepts used in statistics, data sampling, types of sampling,types of data and collection of data.

STAT 101 Lecture collegeINTRODUCTION.pdf

This document provides an introduction to statistics. It discusses that statistics involves collecting, organizing, analyzing, and interpreting quantitative data. It describes two main types of statistics: descriptive statistics which summarizes and describes data, and inferential statistics which makes inferences about a population based on a sample. Some key statistical concepts introduced include population, sample, variables, levels of measurement, and sampling techniques such as random sampling and stratified random sampling.

Statistical Estimation

This document provides an overview of statistical estimation and inference. It discusses point estimation, which provides a single value to estimate an unknown population parameter, and interval estimation, which gives a range of plausible values for the parameter. The key aspects of interval estimation are confidence intervals, which provide a probability statement about where the true population parameter lies. The document also covers important concepts like sampling distributions, the central limit theorem, and factors that influence the width of a confidence interval like sample size. Examples are provided to demonstrate calculating point estimates, confidence intervals, and dealing with independent samples.

Sampling techniques new

This document provides an overview of sampling and key sampling concepts. It defines population and sample, and describes different types of sampling including: probability sampling methods like simple random sampling, systematic random sampling, stratified random sampling, and cluster sampling. It also describes non-probability sampling methods like convenience sampling, quota sampling, and purposive sampling. The document discusses important sampling concepts like sampling frame, sampling error, and determining sample size. It provides examples and limitations of different sampling techniques.

Sampling techniques new

The document discusses sampling methods and concepts. It defines key terms like population, sample, sampling frame and sampling error. It describes different types of sampling including probability sampling methods like simple random sampling, systematic random sampling and cluster sampling. It also discusses non-probability sampling and factors to consider in determining sample size. The document provides guidance on calculating sampling error and outlines principles of good sampling.

STATISTICS-AND-PROBABLITY-A-REVIEW-FOR-SHS.pdf

This document provides an overview of key concepts in statistics and probability. It discusses descriptive statistics, which includes techniques for summarizing and describing numerical data through tables, graphs and charts. It also covers inferential statistics, which allows generalization from samples to populations through hypothesis testing and determining relationships. Key terms are defined, such as data, population, sample, and variable. Common statistical measures like the mean, median and mode are also introduced.

BIOMETRYc(1).pptx

This document defines biometry and summarizes statistical methods for estimating population parameters from sample data. It begins by defining biometry as the application of statistical methods to biological problems, involving the measurement of life. It then discusses two types of estimation: point estimation, which provides a single value as an estimate, and interval estimation, which provides a range of values that the parameter is expected to fall within at a given confidence level. The document provides formulas and examples for constructing confidence intervals to estimate a single mean, the difference between two population means, and other parameters, depending on whether the population standard deviation is known or estimated from sample data, and whether sample sizes are large or small.

BIOMETRYc(1).pptx

This document defines biometry and provides examples of its applications. It begins by defining statistics and its uses in collecting and analyzing numerical data. It then discusses the following key points:
- Biometry refers to the application of statistical methods to solve biological problems, through measuring and analyzing life-related data.
- Early statistical methods for experimental design originated in agricultural research, pioneered by Ronald Fisher in his work analyzing wheat experiments. He developed random assignment, balancing treatments, determining optimal replication, and accounting for variability.
- Biometry is used to estimate population parameters like means from sample data using techniques like point estimation, interval estimation, and hypothesis testing. Estimation accounts for variables like sample size, known or estimated standard

A basic Introduction To Statistics with examples

A basic Introduction To Statistics with examples

Introduction to Biostatistics

Introduction to Biostatistics

LECTURE 3-PREVALENCE& DISEASES OUTBREAK.pdf

LECTURE 3-PREVALENCE& DISEASES OUTBREAK.pdf

Ph250b.14 measures of disease part 2 fri sep 5 2014

Ph250b.14 measures of disease part 2 fri sep 5 2014

San diego

San diego

Statics for management

Statics for management

Sampling Theory Part 1

Sampling Theory Part 1

Bio stat

Bio stat

Sampling

Sampling

Statistics for management assignment

Statistics for management assignment

Seminar-2015

Seminar-2015

SP and R.pptx

SP and R.pptx

Introduction to Statistics (Part -I)

Introduction to Statistics (Part -I)

STAT 101 Lecture collegeINTRODUCTION.pdf

STAT 101 Lecture collegeINTRODUCTION.pdf

Statistical Estimation

Statistical Estimation

Sampling techniques new

Sampling techniques new

Sampling techniques new

Sampling techniques new

STATISTICS-AND-PROBABLITY-A-REVIEW-FOR-SHS.pdf

STATISTICS-AND-PROBABLITY-A-REVIEW-FOR-SHS.pdf

BIOMETRYc(1).pptx

BIOMETRYc(1).pptx

BIOMETRYc(1).pptx

BIOMETRYc(1).pptx

Making sense of citizen science data: A review of methods

My talk at International Congress for Conservation Biology 2015, in Montpellier.
Data collected through citizen science programs allow addressing many important questions in conservation biology related, e.g., to the shift in species range, the ecology of infectious disease or the effects of habitat loss and fragmentation on biodiversity. However, citizen science data are subject to serious statistical challenges when it comes to their analysis and the reliable extraction of the information they contain, mainly due to sampling biases generated by variation in the observation process. Numerous methods have been proposed to address this issue that can be split into two main strategies: either a new approach is developed to deal with a specific problem or an existing approach is used pending some pre-treatment of the data or post-processing of the results. I review these various methods, trying to make the links between them and emphasizing their advantages and drawbacks with respect to the question. I illustrate my talk with case studies drawn for the research conducted in our group, mainly on large carnivores. Based on this review, I end up this contribution by recommendations on the use of existing methods and by suggesting perspectives on future developments.

Dealing with observer bias when mapping species distribution using citizen sc...

Citizen science data can be useful for mapping species distributions but requires dealing with observer bias. The document discusses using a Poisson point process model to map the distribution of brown bears in Greece using citizen science data while accounting for observer bias. Environmental variables like elevation, slope, land cover and distance to roads were used in the model. Distance to roads was found to be an important observer bias variable that affected detection probability rather than actual bear presence. The model results provided coherent maps of bear distribution and intensity compared to hypothetical models using only ecological variables or presence-absence data. Accounting for observer bias through variables like distance to roads is important when using opportunistic citizen science data for species distribution modeling.

Individual Heterogeneity in Capture-Recapture Models

How to assess individual heterogeneity in demographic parameters and detectability using capture-recapture models.

Talk by Laetitia Blanc at ISEC 2014 on improving abundance estimates by combi...

This document discusses combining capture-recapture and occupancy data to improve abundance estimation of wildlife populations. It notes that abundance and distribution are often studied separately but are linked variables. A combined model is presented that uses both individual capture-recapture data to estimate abundance and extensive presence-absence data to model occupancy. This combined approach provides a more precise abundance estimate than using capture-recapture data alone, especially for elusive territorial species where resources limit intensive monitoring.

My talk at EURING 2013 on individual variability in capture-recapture models

This document discusses individual heterogeneity (IH) in capture-recapture models from biological and statistical perspectives. It begins by introducing long-term individual monitoring datasets and some methodological issues when applying capture-recapture models to natural populations, including detectability being less than 1 and IH. The talk then explores several case studies related to accounting for IH, including detecting trade-offs using multistate models, describing senescence using mixture models, and quantifying the heritability of IH using animal models. Throughout, it emphasizes the importance of accounting for IH both statistically and biologically.

HDR Olivier Gimenez

This document discusses capture-recapture models for investigating evolution in natural populations. It begins by outlining some methodological issues with moving studies of evolution from the lab to natural conditions, including detectability being less than 1 and individual heterogeneity. It then presents three case studies: 1) describing senescence using mixture models to account for heterogeneity, 2) detecting trade-offs using individual random effect models for a continuous mixture, and 3) quantifying heritability. The document emphasizes that accounting for sources of individual heterogeneity is important when assessing traits related to fitness in the wild like senescence and trade-offs.

My talk at ISEC 2014 (http://isec2014.sciencesconf.org/) on how to model occu...

This document discusses using hidden Markov models to analyze occupancy data. It describes how occupancy models can be formulated as hidden Markov models, with sites as hidden states that can be occupied or unoccupied over time. Both single-season and dynamic occupancy models are discussed. Modeling occupancy data as hidden Markov models provides a unified framework and links occupancy modeling to capture-recapture methods. Software called E-SURGE that was originally developed for capture-recapture analysis can also be used to fit occupancy models. An example case study uses E-SURGE to model Eurasian lynx occupancy data from France allowing for detection heterogeneity. Extensions to occupancy hidden Markov models including distribution mapping, accounting for lack of independence, and multistate models

Making sense of citizen science data: A review of methods

Making sense of citizen science data: A review of methods

Dealing with observer bias when mapping species distribution using citizen sc...

Dealing with observer bias when mapping species distribution using citizen sc...

Individual Heterogeneity in Capture-Recapture Models

Individual Heterogeneity in Capture-Recapture Models

Talk by Laetitia Blanc at ISEC 2014 on improving abundance estimates by combi...

Talk by Laetitia Blanc at ISEC 2014 on improving abundance estimates by combi...

My talk at EURING 2013 on individual variability in capture-recapture models

My talk at EURING 2013 on individual variability in capture-recapture models

My CNRS interview to get a senior scientist position (directeur de recherche)

My CNRS interview to get a senior scientist position (directeur de recherche)

HDR Olivier Gimenez

HDR Olivier Gimenez

My talk at ISEC 2014 (http://isec2014.sciencesconf.org/) on how to model occu...

My talk at ISEC 2014 (http://isec2014.sciencesconf.org/) on how to model occu...

A hot-Jupiter progenitor on a super-eccentric retrograde orbit

Giant exoplanets orbiting close to their host stars are unlikely to have formed in
their present confgurations1
. These ‘hot Jupiter’ planets are instead thought to have
migrated inward from beyond the ice line and several viable migration channels
have been proposed, including eccentricity excitation through angular-momentum
exchange with a third body followed by tidally driven orbital circularization2,3
. The
discovery of the extremely eccentric (e = 0.93) giant exoplanet HD 80606 b (ref. 4)
provided observational evidence that hot Jupiters may have formed through
this high-eccentricity tidal-migration pathway5
. However, no similar hot-Jupiter
progenitors have been found and simulations predict that one factor afecting the
efcacy of this mechanism is exoplanet mass, as low-mass planets are more likely to
be tidally disrupted during periastron passage6–8
. Here we present spectroscopic and
photometric observations of TIC 241249530 b, a high-mass, transiting warm Jupiter
with an extreme orbital eccentricity of e = 0.94. The orbit of TIC 241249530 b is
consistent with a history of eccentricity oscillations and a future tidal circularization
trajectory. Our analysis of the mass and eccentricity distributions of the transitingwarm-Jupiter population further reveals a correlation between high mass and high
eccentricity.

Rice Genome Project a complete saga .(1).pptx

This slide includes all the data about Rice Genome Project which is a International consortium for sequencing whole rice genome .All the salient findings . The timelines , the countries involved including list of all laboratories and their role , with the objective and role of India in the project , India's work and institutions involved , timelines as a graphical representations , and the genome size no of genes , information about transposons , gene families and also it includes methodologies like shot gun approach they have used for sequencing , and how fidelity is maintained , how much gap they found , how many genomics libraries used , BAC ,PAC fosmids that are used, and all the bioinformatics tools that are used for contig assembly and gene annotation , it includes all maps and a simple description of the findings , based on the paper published bu IRGSP on 2004 , the total referencing is based on that paper .

Burn child health Nursing 3rd year presentation..pptx

Easy learning...pptx..for professional growth and development ❣️🎊

End of pipe treatment: Unlocking the potential of RAS waste - Carlos Octavio ...

End of pipe treatment: Unlocking the potential of RAS waste - Carlos Octavio ...Faculty of Applied Chemistry and Materials Science

End of pipe treatment: Unlocking the potential of RAS waste
Carlos Octavio Letelier-Gordo, DTU Aqua, DenmarkReview Article:- A REVIEW ON RADIOISOTOPES IN CANCER THERAPY

A REVIEW ON RADIOISOTOPES IN CANCER THERAPY

Concept of Balanced Diet & Nutrients.pdf

This pdf is about the introduction to concept of Balanced Diet & Nutrients.
For more details visit on YouTube; @SELF-EXPLANATORY; https://www.youtube.com/channel/UCAiarMZDNhe1A3Rnpr_WkzA/videos
Thanks...!

The Next-Gen Innovative Therapeutic Potential of Probiotics: Insights into Gu...

The Next-Gen Innovative Therapeutic Potential of Probiotics: Insights into Gu...Dr. Lenin Kumar Bompalli

The innovative therapeutic potential of probiotics has garnered significant interest, especially in the context of modulating gut microbiota and promoting health. Here's an explanation of how next-generation probiotics are being explored for their therapeutic benefits:
1. Understanding Probiotics and Gut Microbiota
Probiotics are live microorganisms that, when administered in adequate amounts, confer a health benefit on the host. They are primarily known for their role in maintaining and restoring gut microbiota balance.
Gut microbiota refers to the trillions of microorganisms residing in the human gastrointestinal tract. These microorganisms play crucial roles in digestion, immune function, and overall health.
2. Modulation of Gut Microbiota
Probiotics can modulate gut microbiota in several ways:
Restoring Balance: They help in restoring the natural balance of gut bacteria, especially after disruptions caused by antibiotics or illness.
Inhibiting Pathogens: Probiotics can produce substances that inhibit harmful pathogens, thus preventing infections.
Enhancing Barrier Function: They can strengthen the intestinal barrier, reducing the risk of harmful substances entering the bloodstream.
3. Health Promotion and Therapeutic Potential
The health benefits of probiotics extend beyond gut health, influencing various aspects of overall well-being:
Digestive Health: Probiotics are effective in managing conditions like irritable bowel syndrome (IBS), inflammatory bowel disease (IBD), and diarrhea.
Immune System Support: By interacting with the gut-associated lymphoid tissue (GALT), probiotics can enhance immune responses and reduce inflammation.
Mental Health: There is growing evidence linking gut microbiota to mental health, often referred to as the "gut-brain axis." Probiotics may help alleviate symptoms of anxiety, depression, and stress.
Metabolic Health: Probiotics can influence metabolic processes, potentially aiding in weight management, reducing cholesterol levels, and improving glucose metabolism.
Skin Health: By modulating systemic inflammation, probiotics can benefit skin conditions like eczema and acne.
Allergies and Autoimmune Diseases: Probiotics might help in managing allergies and autoimmune diseases by modulating immune responses.
4. Next-Generation Probiotics
Traditional probiotics primarily include strains from the genera Lactobacillus and Bifidobacterium. Next-generation probiotics encompass a broader range of beneficial microbes, including:
Bacterial Strains: New strains such as Akkermansia muciniphila and Faecalibacterium prausnitzii show promise in modulating gut health and metabolic functions.
Fecal Microbiota Transplantation (FMT): This involves transferring stool from a healthy donor to a patient, aiming to restore a healthy gut microbiota composition.
Prebiotics and Synbiotics: Prebiotics are non-digestible food components that promote the growth of beneficial bacteria. 17. 20240529_Ingrid Olesen_MariGreen summer school.pdf

Moving beyond agriculture and aquaculture to integrated sustainable food systems as part of a circular bioeconomy

Types of Hypersensitivity Reactions.pptx

Hypersensitivity as an immunological dysfunction is defined as exaggerated or inappropriate response of the immune system. Hypersensitivity can be classified into four types; namely, type I (Immediate), type II (antibody-mediated), type III (immune complex-mediated), and type IV (cell-mediated or delayed-type) hypersensitivity.
Type I hypersensitivity or allergy, the most common immune disorder, is mainly mediated by immunoglobulin (Ig)E and mast cells. It can cause anaphylaxis, food allergy, and asthma.
Type II hypersensitivity can lead to tissue damage by three main mechanisms: (1) direct cellular destruction (e.g., autoimmune hemolytic anemia and immune thrombocytopenia, (2) inflammation (e.g., Goodpasture's syndrome and acute rheumatic fever), and (3) disrupting cellular function (e.g., myasthenia gravis and Graves’ disease).
Type III hypersensitivity is caused by excess production of immune complexes or impaired clearance of them and includes serum sickness, systemic lupus erythematosus, and post-streptococcal glomerulonephritis.
Type IV hypersensitivity is mediated by T cells and macrophages, causing diseases like multiple sclerosis and rheumatoid arthritis.

Traditional, current and future use of fish and seaweed for fertilisation - ...

Traditional, current and future use of fish and seaweed for fertilisation - ...Faculty of Applied Chemistry and Materials Science

Traditional, current and future use of fish and seaweed for fertilisation
Anne-Kristin Løes, Norwegian Centre for Organic Agriculture (NORSØK), NorwayFish in the Loop: Exploring RAS - Julie Hansen Bergstedt

Fish in the Loop: Exploring RAS - Julie Hansen BergstedtFaculty of Applied Chemistry and Materials Science

Fish in the Loop: Exploring RAS
Julie Hansen Bergstedt, DTU Aqua, Denmark
All-domain Anomaly Resolution Office Supplement to Oak Ridge National Laborat...

In 2022, The All-domain Anomaly Resolution Office (AARO) contracted with Oak Ridge
National Laboratory (ORNL) to conduct materials testing on a magnesium (Mg) alloy specimen.
This specimen has been publicly alleged to be a component recovered from a crashed
extraterrestrial vehicle in 1947, and purportedly exhibits extraordinary properties, such as
functioning as a terahertz waveguide to generate antigravity capabilities. In April 2024, ORNL
produced a summary of findings documenting the laboratory’s methodology to assess this
specimen’s elemental and structural characteristics, available on AARO’s website.
ORNL assessed this specimen to be terrestrial in origin and that it does not meet the theoretical
requirements to function as a terahertz (THz) waveguide. AARO concurs with ORNL’s
assessment and provides this supplementary material to add historical context to account for its
likely origin. The specimen’s characteristics are consistent with Mg alloy research and
development projects and experimental manufacturing methods in the mid-20th century.

Surface properties of the seas of Titan as revealed by Cassini mission bistat...

Saturn’s moon Titan was explored by the Cassini spacecraft from 2004 to 2017.
While Cassini revealed a lot about this Earth-like world, its radar observations
could only provide limited information about Titan’s liquid hydrocarbons seas
Kraken, Ligeia and Punga Mare. Here, we show the results of the analysis of the
Cassini mission bistatic radar experiments data of Titan’s polar seas. The dualpolarized nature of bistatic radar observations allow independent estimates of
effective relative dielectric constant and small-scale roughness of sea surface,
which were not possible via monostatic radar data. We find statistically significant variations in effective dielectric constant (i.e., liquid composition),
consistent with a latitudinal dependence in the methane-ethane mixing-ratio.
The results on estuaries suggest lower values than the open seas, compatible
with methane-rich rivers entering seas with higher ethane content. We estimate small-scale roughness of a few millimeters from the almost purely
coherent scattering from the sea surface, hinting at the presence of capillary
waves. This roughness is concentrated near estuaries and inter-basin straits,
perhaps indicating active tidal currents.

Phytoremediation: Harnessing Nature's Power with Phytoremediation

This document provides an overview of phytoremediation, which uses plants to remove contaminants from soil, sediment, or water. It discusses the need for new remediation techniques, describes various phytoremediation processes like phytoextraction and rhizofiltration, and covers important concepts like hyperaccumulators, biotechnology applications, case studies, and advantages/limitations. The author aims to explain the mechanisms, history, types of plants used, and future research directions of this eco-friendly approach to environmental cleanup.

Ancient Theory, Abiogenesis , Biogenesis

Ancient Theory
Abiogenesis
Biogenesis
Origin of life
Evolution of Prokaryotic
Evolution of Eukaryotes
Modern theory
Evolution of modes of Nutrition

Celebrity Girls Call Navi Mumbai 🎈🔥9920725232 🔥💋🎈 Provide Best And Top Girl S...

Celebrity Girls Call Navi Mumbai 🎈🔥9920725232 🔥💋🎈 Provide Best And Top Girl Service And No1 in City

Analytical methods for blue residues characterization - Oana Crina Bujor

Analytical methods for blue residues characterization - Oana Crina BujorFaculty of Applied Chemistry and Materials Science

Analytical methods for blue residues characterization
Oana Crina Bujor, University of Agronomic Sciences and Veterinary Medicine (USAMV), RomaniaSpeed-accuracy trade-off for the diffusion models

The presentation of Frontiers in Nonequilibrium Physics at YITP about the preprint https://arxiv.org/abs/2407.04495.
Thermodynamic trade-off between the accuracy of the data generation and the diffusion speed for the diffusion models. We show thermodynamically that the optimal transport provides the most accurate data generation.

A hot-Jupiter progenitor on a super-eccentric retrograde orbit

A hot-Jupiter progenitor on a super-eccentric retrograde orbit

Rice Genome Project a complete saga .(1).pptx

Rice Genome Project a complete saga .(1).pptx

Burn child health Nursing 3rd year presentation..pptx

Burn child health Nursing 3rd year presentation..pptx

End of pipe treatment: Unlocking the potential of RAS waste - Carlos Octavio ...

End of pipe treatment: Unlocking the potential of RAS waste - Carlos Octavio ...

Review Article:- A REVIEW ON RADIOISOTOPES IN CANCER THERAPY

Review Article:- A REVIEW ON RADIOISOTOPES IN CANCER THERAPY

Concept of Balanced Diet & Nutrients.pdf

Concept of Balanced Diet & Nutrients.pdf

Post RN - Biochemistry (Unit 7) Metabolism

Post RN - Biochemistry (Unit 7) Metabolism

The Next-Gen Innovative Therapeutic Potential of Probiotics: Insights into Gu...

The Next-Gen Innovative Therapeutic Potential of Probiotics: Insights into Gu...

17. 20240529_Ingrid Olesen_MariGreen summer school.pdf

17. 20240529_Ingrid Olesen_MariGreen summer school.pdf

Types of Hypersensitivity Reactions.pptx

Types of Hypersensitivity Reactions.pptx

Traditional, current and future use of fish and seaweed for fertilisation - ...

Traditional, current and future use of fish and seaweed for fertilisation - ...

Fish in the Loop: Exploring RAS - Julie Hansen Bergstedt

Fish in the Loop: Exploring RAS - Julie Hansen Bergstedt

All-domain Anomaly Resolution Office Supplement to Oak Ridge National Laborat...

All-domain Anomaly Resolution Office Supplement to Oak Ridge National Laborat...

Surface properties of the seas of Titan as revealed by Cassini mission bistat...

Surface properties of the seas of Titan as revealed by Cassini mission bistat...

Phytoremediation: Harnessing Nature's Power with Phytoremediation

Phytoremediation: Harnessing Nature's Power with Phytoremediation

Ancient Theory, Abiogenesis , Biogenesis

Ancient Theory, Abiogenesis , Biogenesis

VIII-Geography FOR CBSE CLASS 8 INDIA.pdf

VIII-Geography FOR CBSE CLASS 8 INDIA.pdf

Celebrity Girls Call Navi Mumbai 🎈🔥9920725232 🔥💋🎈 Provide Best And Top Girl S...

Celebrity Girls Call Navi Mumbai 🎈🔥9920725232 🔥💋🎈 Provide Best And Top Girl S...

Analytical methods for blue residues characterization - Oana Crina Bujor

Analytical methods for blue residues characterization - Oana Crina Bujor

Speed-accuracy trade-off for the diffusion models

Speed-accuracy trade-off for the diffusion models

- 1. Using informative priors to improve parameters estimation in capture-recapture models Blaise Piédallu PhD Student Supervisors : Olivier Gimenez Pierre-Yves Quenette
- 2. Population of interest -Brown bears (Ursus arctos) in the French and Spanish Pyrénées -Population size : about 25 individuals in 2013 -Individuals are detected and identified through different methods (camera pictures, genetic sampling of hair and faeces) -2 population cores or « Regions » (Western and Central- Eastern), without communication (Western – 2 individuals in 2013 – and Central-Eastern – ~23 individuals in 2013)
- 3. Population of interest Objectives : - Estimate population size, test for difference in Regions - Since the population is small, use an informative prior to improve the precision
- 4. Dataset Capture-Recapture data during 25 years (1989 – 2013) A Jolly-Seber model is used to estimate population size - Capture-Recapture on n different occasions (here, n=25) - Open population: immigrations (births/reintroductions) and emigrations (deaths) State-Space Model : Detected (= 1) Not detected (= 0) p 1 - p 1 Not detected (= 0) Survived Time t Died f 1 - f Hidden information Observed information
- 5. Dataset Capture-Recapture data during 25 years (1989 – 2013) A Jolly-Seber model is used to estimate population size - Capture-Recapture on n different occasions (here, n=25) - Open population: immigrations (births/reintroductions) and emigrations (deaths) State-Space Model : Detected (= 1) Not detected (= 0) p 1 - p 1 Not detected (= 0) Survived Time t Died f 1 - f Hidden information Observed information
- 6. Model Selection The Bayesian computation was performed with the softwares -R- and JAGS. Tested models Survival Detection r . r + T + r.T r + T r T . r : « Region » effect T : Time effect . : no effect Survival probability : logit(phi[i,t]) <- alpha[1] + alpha[2]*cov.region[i] Detection probability : logit(p[i,t]) <- alpha[3] + alpha[4]*cov.region[i] + alpha[5]*t + alpha[6]*t*cov.region[i] + eps[i]
- 7. Model Selection Model selection by estimating posterior model probabilities (Kuo and Mallick, 1998) Survival probability : logit(phi[i,t]) <- alpha[1] + w[1]*alpha[2]*cov.region[i] Detection probability : logit(p[i,t]) <- alpha[3] + w[2]*alpha[4]*cov.region[i] + w[3]*alpha[5]*t + w[4]*alpha[6]*t*cov.region[i] + eps[i] Indicator variables - w ~ dbern(0.5) Multiplies every relevant parameter
- 8. Model Selection MCMC sampling Posterior model probability = Number of iterations using this model Total number of iterations
- 9. Model Selection Results : r . r + T + r.T 0,01027 0,05487 r + T 0,06833 0,36239 r 0,04008 0,18398 T 0,01216 0,05809 . 0,02383 0,12107 Survival Capture Model selected : Survival = f(.), Capture = f(r+T) Some models with no significance are ignored: the intersect of r and T only has a meaning if both r and T are used Theoretical number of models = 24 = 16
- 10. Estimated survival Estimated density with an uninformative prior U(0,1) f = 0.94 ± 0.015
- 11. Using informative priors Two priors for Survival were used for the next simulations : -A non informative prior U(0,1) -An informative prior B(a,b), with a and b chosen in order to get a mean of 0.9 and a standard deviation of 0.025
- 12. Using informative priors Entire dataset n = 25 years Both population cores No difference in population size estimation No difference in standard deviation
- 13. Using informative Priors - Splitting the dataset Dataset split n = 25 years Eastern population only No difference in population size estimation No difference in standard deviation
- 14. Using informative Priors - Splitting the dataset Dataset split n = 25 years Western population only No difference in population size estimation No difference in standard deviation
- 15. Using informative Priors - Splitting the dataset Dataset split n = 15 years (89-03) Both population cores Improvement in standard deviation by using informative priors
- 16. Using informative Priors - Splitting the dataset Dataset split n = 15 years (04-13) Both population cores Improvement in standard deviation by using informative priors Difference in population size estimation
- 17. Conclusion What can we say about informative priors ? -Even relatively small datasets may contain enough data in order to be used - In the case of the French Brown Bear, the information seems to come from the length of the study (over 25 years) -In the last 10 years, monitoring of the population has greatly increased – more people involved, improving the search for genetic samples in the Pyrénées -Informative priors are useful to create a more complex model including more parameters on a smaller timeframe
- 18. Conclusion What to do next ? Check if an informative prior has influences model selection Check the influence of an informative prior on a more complex model : -Add more age classes -Add gender Use the parameter estimates in order to check the influence of future reintroductions Perform a viability analysis of the population using the informative priors
- 19. The End From Pyrénée, written by Régis Loisel, drawn by Philippe Sternis THANK YOU FOR YOUR ATTENTION !